{"timestamp":"2025-09-22T13:05:15.878Z","type":"session_meta","payload":{"id":"01997187-4a1a-7f13-a0a0-66ae52d574fe","timestamp":"2025-09-22T13:05:15.802Z","cwd":"C:\\GitCode\\paiban","originator":"codex_cli_rs","cli_version":"0.39.0","instructions":"﻿# Repository Guidelines\r\n\r\n## Project Structure & Module Organization\r\nSource lives under the solution `NPP.SmartSchedue.sln`. `NPP.SmartSchedue.Host` hosts the ASP.NET Core app, with environment settings in `appsettings*.json`, seed scripts in `ConfigCenter` and `InitData`, and static assets in `wwwroot`. Domain services sit in `NPP.SmartSchedue.Api`, organized into `Core` (attributes, constants), `Services` (business logic), and `Repositories` (data access). Shared contracts, DTOs, and interfaces reside in `NPP.SmartSchedue.Api.Contracts`; keep cross-project types there. Tests are in `NPP.SmartSchedue.Tests`, with reusable fixtures in `BaseTest` and service-focused integration suites under `Services`.\r\n\r\n## Build, Test, and Development Commands\r\n- `dotnet restore NPP.SmartSchedue.sln` restores all project dependencies.\r\n- `dotnet build NPP.SmartSchedue.sln -c Release` validates compilation; use `-c Debug` during local iteration.\r\n- `dotnet run --project NPP.SmartSchedue.Host` boots the API with the default profile; add `--launch-profile Development` when you need local CAP setups.\r\n- `dotnet test NPP.SmartSchedue.Tests/NPP.SmartSchedue.Tests.csproj --logger \"trx;LogFileName=test-results.trx\"` exercises the xUnit suites and saves artifacts for PR review.\r\n\r\n## Coding Style & Naming Conventions\r\nTarget .NET 9 defaults: four-space indentation, braces on new lines, and explicit namespaces. Use `PascalCase` for classes, public members, and DTOs, `camelCase` for locals and private fields, and prefix interfaces with `I` as seen in `Api.Contracts`. Suffix asynchronous methods with `Async`, and keep request/response models in the Contracts project. Run `dotnet format` before committing to enforce spacing and using-order consistency.\r\n\r\n## Testing Guidelines\r\nWrite xUnit tests alongside existing patterns such as `EquipmentMaintenanceIntegrationTest`. Name new files `{Feature}Test.cs` or `{Feature}IntegrationTest.cs`, group helper logic in `BaseTest`, and rely on `Microsoft.AspNetCore.Mvc.Testing` for host bootstrapping. Aim to cover new service paths and repository edge cases; add mocks via Moq where external systems (CAP, RabbitMQ, MySQL) would otherwise be required.\r\n\r\n## Commit & Pull Request Guidelines\r\nPrefer descriptive commits (`feat(scheduling): add notification workflow`) instead of numeric placeholders seen in recent history. Bundle related changes, include migration or config adjustments in the same commit, and reference tracking IDs where relevant. Pull requests should outline the change, list manual or automated test evidence, mention configuration impacts (CAP, FreeSql, message queues), and supply screenshots for any UI updates under `wwwroot`.\r\n\r\n## Configuration & Environment Notes\r\nKeep secrets out of `appsettings.json`; use user secrets or environment variables for connection strings and CAP brokers. Document any required RabbitMQ or MySQL endpoints in the PR. When adding assets or scripts, mirror the structure under `ConfigCenter`, `InitData`, and `wwwroot` so the publish targets continue copying them correctly.\r\n","git":{"commit_hash":"2b3f9acdce96bfa8be7e2cdb15dd399be53e969d","branch":"master","repository_url":"https://git.comzg.com/WuXi/paiban.git"}}}
{"timestamp":"2025-09-22T13:05:15.879Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"<user_instructions>\n\n﻿# Repository Guidelines\r\n\r\n## Project Structure & Module Organization\r\nSource lives under the solution `NPP.SmartSchedue.sln`. `NPP.SmartSchedue.Host` hosts the ASP.NET Core app, with environment settings in `appsettings*.json`, seed scripts in `ConfigCenter` and `InitData`, and static assets in `wwwroot`. Domain services sit in `NPP.SmartSchedue.Api`, organized into `Core` (attributes, constants), `Services` (business logic), and `Repositories` (data access). Shared contracts, DTOs, and interfaces reside in `NPP.SmartSchedue.Api.Contracts`; keep cross-project types there. Tests are in `NPP.SmartSchedue.Tests`, with reusable fixtures in `BaseTest` and service-focused integration suites under `Services`.\r\n\r\n## Build, Test, and Development Commands\r\n- `dotnet restore NPP.SmartSchedue.sln` restores all project dependencies.\r\n- `dotnet build NPP.SmartSchedue.sln -c Release` validates compilation; use `-c Debug` during local iteration.\r\n- `dotnet run --project NPP.SmartSchedue.Host` boots the API with the default profile; add `--launch-profile Development` when you need local CAP setups.\r\n- `dotnet test NPP.SmartSchedue.Tests/NPP.SmartSchedue.Tests.csproj --logger \"trx;LogFileName=test-results.trx\"` exercises the xUnit suites and saves artifacts for PR review.\r\n\r\n## Coding Style & Naming Conventions\r\nTarget .NET 9 defaults: four-space indentation, braces on new lines, and explicit namespaces. Use `PascalCase` for classes, public members, and DTOs, `camelCase` for locals and private fields, and prefix interfaces with `I` as seen in `Api.Contracts`. Suffix asynchronous methods with `Async`, and keep request/response models in the Contracts project. Run `dotnet format` before committing to enforce spacing and using-order consistency.\r\n\r\n## Testing Guidelines\r\nWrite xUnit tests alongside existing patterns such as `EquipmentMaintenanceIntegrationTest`. Name new files `{Feature}Test.cs` or `{Feature}IntegrationTest.cs`, group helper logic in `BaseTest`, and rely on `Microsoft.AspNetCore.Mvc.Testing` for host bootstrapping. Aim to cover new service paths and repository edge cases; add mocks via Moq where external systems (CAP, RabbitMQ, MySQL) would otherwise be required.\r\n\r\n## Commit & Pull Request Guidelines\r\nPrefer descriptive commits (`feat(scheduling): add notification workflow`) instead of numeric placeholders seen in recent history. Bundle related changes, include migration or config adjustments in the same commit, and reference tracking IDs where relevant. Pull requests should outline the change, list manual or automated test evidence, mention configuration impacts (CAP, FreeSql, message queues), and supply screenshots for any UI updates under `wwwroot`.\r\n\r\n## Configuration & Environment Notes\r\nKeep secrets out of `appsettings.json`; use user secrets or environment variables for connection strings and CAP brokers. Document any required RabbitMQ or MySQL endpoints in the PR. When adding assets or scripts, mirror the structure under `ConfigCenter`, `InitData`, and `wwwroot` so the publish targets continue copying them correctly.\r\n\n\n</user_instructions>"}]}}
{"timestamp":"2025-09-22T13:05:15.879Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"<environment_context>\n  <cwd>C:\\GitCode\\paiban</cwd>\n  <approval_policy>never</approval_policy>\n  <sandbox_mode>danger-full-access</sandbox_mode>\n  <network_access>enabled</network_access>\n  <shell>powershell.exe</shell>\n</environment_context>"}]}}
{"timestamp":"2025-09-22T13:06:05.810Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"分析  LinearProgrammingEngine，"}]}}
{"timestamp":"2025-09-22T13:06:05.810Z","type":"event_msg","payload":{"type":"user_message","message":"分析  LinearProgrammingEngine，","kind":"plain"}}
{"timestamp":"2025-09-22T13:06:05.810Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:18.154Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Locating LinearProgrammingEngine files**"}}
{"timestamp":"2025-09-22T13:06:18.809Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3267,"cached_input_tokens":2432,"output_tokens":375,"reasoning_output_tokens":320,"total_tokens":3642},"last_token_usage":{"input_tokens":3267,"cached_input_tokens":2432,"output_tokens":375,"reasoning_output_tokens":320,"total_tokens":3642},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:18.809Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Locating LinearProgrammingEngine files**"}],"content":null,"encrypted_content":"gAAAAABo0UnLSCTyBFBggMF4KvPFrj-W7vn-BICxVaEb3LOsC0wDoYZtCtPWdG9pJzmOpam6H4PBupOnIcdO1uxlxml_Iv3i-MLeG-m-1lQzRG_fox__kgXK_YOu2JZ73XQu-AQIW8LCg0uFvPP67rrh6kJK0jcT7fOOYID1OGyb7QbtZ3KcdTKfD9MzQJI2jG7P_--kwueTYOX3DMzH1wHl77nWACWahSwXWTa7Cf-HsITK4etGBckcTvMayQfVfKQwj-r-afFmngtbJfuz6207b70lXZXp104Rp3IvmvwWrrhwN_HZ4wbddvF1MoBBAwSi9l-n1MS57a2zvhoxss44WB7aEIF6etqCZiCmltV_uA_wJpRMYld83-gu8eGCro1k3-Wk5XXEMFpR5LpbJGDCY9oQFIiBYCEValXVrlgJuZZ7dSc_IsNX3X1F6eu-1OC5r5ZQc9jGfJqWtajg1RCcHnDCtNwY6RUFOGLIcK3uhC7C_9SVeOfOTesfcItEKkGHyZnaQ3D-g2w-Kgqdtp1NxdZ3YeO5HGdeirJsFS4f7I84RgBUy9GbcJBxVaR9BqoAn0Q8lD8NtU4mca6ESzM12WiEd3zoPBnzZTApZuPSM0ZK9oerKMVkKy2wV_WVmqdOqCwsoE8rHbUZFS0YvZRZlvfMp6e6vWruTeyyJ_QIgHF9UlGtr03vFSmycfPCmCF1YYdK5wY35Iu7zjHGbUUzl2TTWsnP1AqkwYKSNgG7RhjQeQQXQ2vuExcOvaaYH3VES5ouGtUsW3baQfpC63K5uI5Zt5X7xesLcCKrnLHizx1sGO3mJ2DJvidSRve2dAb7EjLQuX-0xvzdk2C4dWZsUAI9BWpMh0RjzsJKrI026_1YW8_AQCLev0pCFpYOhrkS5Oqfssl21vOETIcNgICSw3-yVPrYq9kzVS67PSYvF3Bm-brM3rvkNaSLm_8Ea_dVdOL32Opy2otrYcf9oZFJTWpFJx4KAhE7XALo1rK32OvycXF2CTGcRqREFhcEJyZYfDAIEkYC80ukh7r_PFgnxX2wTgwwoaiGQvINcb9V1NIbNv50i_GKCdtCXui8f_Uu5uiT-fATZm1KZNtL0aFIF-tq8Xw_hNK4N8sT6UDt8plYAY2zEaPKLBG55d24KGDNtgCdCJTtFfb-fg77JtOnBNhXJrs7UCwEt1MLNcV5a9cn3t0Nc1aj9vkqPrxvlmxOcGImrn2sFE44ogeTcv6TVsL-twtGIOuzbq7omfkfVblU1YOz1lB2bWVRNw5i_wrJN7GH9w6A0foHICVlQpWnpGTN2vcM_biXA5Eze-Dl_bfhqfP-KzgEePb3s_q2uMnDqxWavFA_pYFHXkQTZiwMnEor6oXmrlji2bN3yW0ikom56BgTL0GUuddjzDddYGc10dZGfHEE2d1dBTx8Z7dTs3DM9AZSyArffy7RkjN7cPEj7k2eAXEh2zru4gFcx3i20S4LNz-1HjN7EbYa7F3p1AE-IIQg6dAPKFNuTun9sTy3SHOAGf_cXlclDJaiHDSXHc2CGcL9lHfwCGdHOJUnVd4CO-zfuYS648iq4cL_ZReasGA3xoWw-XJARmNJMMx3dwVQThkX3CKz4UGvCAztlMNNMr2y26iUwLszbYkuJ9F9j81fxw29YIplO6zg6Ir3M14h0Yp4RUjrSlYexNPc0jG2DReWgerGSDfZtTI2Ha2k_D-sttcX8Vo_5lIl8PWNJLMflPCXvDw21xyrRPjR58Xdlx2hdUcpjSwCA-B6OTm7sQ6J4FSBoVEUV8N_bTIpyzf5ZXHbnWqFWkREHY81AGeSECIEvjbpegCI4gFAzQBZIskkibGzYC8vX2-5gkBxAlYbI92C2B5Y5kXgwUFH_VseqajriUIUY9UW0T5kwn_0qWkTPnOz0vcQX3dS2IkSTiB_tmonfKhL7X3xZ3KAgLoVEccDs5Xwko14JVM5GWVAkw1dkxqlUi6HEBWGl1GKrNlQDijPHSho4kC9gTJHZ3m45TXCszdt9VQnmAvqA4LbzPh06EYrU4tPF0I0ETj5_WJSbbS6CqOeyOKJa9QnEXm7tRp1IIQjjwFrhGwu_anlvFfF2C-nwBMBeNBZEhuWsFwgOAK6JPker3NjYpztY1FJj0KM4qI-2Xodica9B--3iap-YryeV_tiwWmIxDgKyq98uLWhcms641Zp52JL6vXZob2Gb0XUuR6exJIs3wk9QBxQJCTzkFp94tHd65aACGi4Mrljw_rZeD4uzOUponlSkRiyyIZuo1jLl4VTdTTjxwl8uG8VBu2tRh2eu7mT1xx2N4cd3Q1o_OOLgRIl2oRqihyt6Pl921ne4NJPlQsLNRehfAMFr0iY5N92POoSznApNWqIc24-dxkqyYnneodp6G4uHrs9cnrl4H_8Ktka_eUdyHbygVXwIwvqO6pdHKqoMkVUMrmOb6iNXSkKJ6YdL6rRJdrBPOUIatCoQI043Ddyki05Bm3DDXwp_6zApNUlHUK88AcG1ywh3N9YG_R5QU-D7KNJrVhPe-1kcR4FXtt--aRVhfoiYIm9Z2vDJZcIzt9QgtdYvXImKAsNHBH2XNSta8ZoHHXwf-BZ28zmLELNqEOa2GXooANiie53II-GoHiI8mHkOCoOsdlWbrDiZ77-TQ7w_OcLM2w1d8Efs1g-E1Q="}}
{"timestamp":"2025-09-22T13:06:18.809Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"rg --files -g *LinearProgrammingEngine*\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_0arT4ZGwTCUVgxBjbPdfUkc6"}}
{"timestamp":"2025-09-22T13:06:18.809Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_0arT4ZGwTCUVgxBjbPdfUkc6","output":"{\"output\":\"NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:06:18.809Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:20.275Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Opening file for analysis**"}}
{"timestamp":"2025-09-22T13:06:21.225Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":6958,"cached_input_tokens":6016,"output_tokens":443,"reasoning_output_tokens":320,"total_tokens":7401},"last_token_usage":{"input_tokens":3691,"cached_input_tokens":3584,"output_tokens":68,"reasoning_output_tokens":0,"total_tokens":3759},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:21.225Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Opening file for analysis**"}],"content":null,"encrypted_content":"gAAAAABo0UnNee5Aeh5WpjuzTYfEfMRe30O1SSZdqjs5d9DgGErLkGXSN41KvsOnKnCxZpwsFjcMtRWDzL7MJZsFhCsmS6Em2LO3zYFnegxQkHoWxa-pVPb1A_TWnGdMCVwG_ijzWQIR6FSXIKJFrGWrmwJ7Fj7pbgPqSELeHQfo33Q_PZA0Q2QNQNTcanB6WTY1DGyXQNrvcmbMFppiqK8iLSKtPjBIV3H5IvKqNOMtXIQIrjzgLPWaP3_PKiCRUDs-lF0BRqS1UKZFCSGCYm2C8JbgI5W7-JiqFblPmkkUDd165Z2l4U8TvYBCjXcl0pR_CF54rh67BCWflE8K5pxSLWXdHcPAd3a97zN01n50Zw9eQNuypoqOm5ebu2Bf-8pR8Ssq8uycXlO1ZwsUgAcO1hEf1kftOGdpX_N_AH77l1V74sTQaE1_RkeWwbwCrlcXKz1sQb9xewgbaaEmlrTfumWn8y_NVsqEcCX7JyzAyxpkK_m4bPUFjjqyI01W34rK4EM1lZXIRpn8eAdMSlvmknheRTbrlYDOo1aMDXjAIidNHjoGGrfywGZ_louljmxfQsiE9dUyblzOOGrEdZNn7BH0MjT6Qg-t1MYHWg2XEMOlZg5dM8d-WAOsG4fQvIOAeWM1C7hG1JBWPM4p3mn-uSPpo-_imIZqss3MqNFqNA58t-0umwVXCnqNEf_jBGFFYfWrb1n1ETLRED7Aicah2RBEC1pAYjotmYwmInOcHSwrepY8fhgiOEOb9_SQcZto7c0z1X_H3CP1OJCymmBhQQZr3qKoOD-6Qm0lHoiI4RP15Xg5ZovY8C8DYPBZd9iqmuT1K3GNMzhTEZjE3eKarp29FR7g8DsSTOYHGoF4Y9hV2S_we3BMtzwbaS3HM9kKpW1g_6PxA8eks72jpNdCpIl3Jlki1LXnEnNFAbb63P6ZFleyiq8YAxqcywR4DumUx23h8gOKHyGFiej6uZ3qrIfN0mg-YA=="}}
{"timestamp":"2025-09-22T13:06:21.225Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_tXhLGFwEqg9EC5ZXyTieZ3Y0"}}
{"timestamp":"2025-09-22T13:06:21.225Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_tXhLGFwEqg9EC5ZXyTieZ3Y0","output":"{\"output\":\"using System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing System.Threading.Tasks;\\nusing Google.OrTools.LinearSolver;\\nusing Microsoft.Extensions.Logging;\\nusing NPP.SmartSchedue.Api.Contracts.Domain.Work;\\nusing NPP.SmartSchedue.Api.Contracts.Domain.Personnel;\\nusing NPP.SmartSchedue.Api.Contracts.Services.Integration.Internal;\\nusing NPP.SmartSchedue.Api.Contracts.Services.Integration.Output;\\n\\nnamespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\n{\\n    /// <summary>\\n    /// 增强版线性规划引�?- 实现遗传算法中的所有复杂约�?\\n    /// 核心功能：三级约束体�?+ 多维度目标优�?+ 完整业务规则支持\\n    /// 设计理念：将遗传算法的启发式约束转换为严格的数学约束，实现精确求�?\\n    /// 技术架构：Google OR-Tools + 分层约束建模 + 自适应权重优化\\n    /// 业务价值：提供数学最优解，替代遗传算法的近似解，提升调度质量\\n    /// </summary>\\n    public class LinearProgrammingEngine\\n    {\\n        private readonly ILogger<LinearProgrammingEngine> _logger;\\n        private readonly ShiftRuleValidationEngine _shiftRuleEngine;\\n        \\n        // 求解器配�?- 增强版配置支持复杂约�?\\n        private const string SOLVER_TYPE = \\\"CBC_MIXED_INTEGER_PROGRAMMING\\\";\\n        private const double OPTIMALITY_TOLERANCE = 1e-6;\\n        private const int MAX_SOLVER_TIME_SECONDS = 600; // 增加�?0分钟支持复杂约束\\n        private const int MAX_CONSTRAINT_COUNT = 50000;   // 最大约束数量限�?\\n        \\n        // 约束权重配置 - 对应遗传算法的三级约束体�?\\n        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 基础约束严厉惩罚\\n        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 组合约束重度惩罚  \\n        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 业务约束中度惩罚\\n        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 公平性奖励系�?\\n\\n        public LinearProgrammingEngine(\\n            ILogger<LinearProgrammingEngine> logger,\\n            ShiftRuleValidationEngine shiftRuleEngine)\\n        {\\n            _logger = logger ?? throw new ArgumentNullException(nameof(logger));\\n            _shiftRuleEngine = shiftRuleEngine ?? throw new ArgumentNullException(nameof(shiftRuleEngine));\\n        }\\n\\n        /// <summary>\\n        /// 执行增强版线性规划优�?- 实现遗传算法等价的约束体�?\\n        /// 业务流程：构建决策变�?�?三级约束建模 �?多目标优�?�?精确求解 �?解析结果\\n        /// 核心创新：将启发式约束转换为线性约束，实现数学最优解\\n        /// </summary>\\n        public async Task<GlobalOptimizedSolution> OptimizeAsync(GlobalAllocationContext context)\\n        {\\n            var stopwatch = System.Diagnostics.Stopwatch.StartNew();\\n            \\n            try\\n            {\\n                _logger.LogDebug(\\\"🚀 启动增强版线性规划优�?- 任务数：{TaskCount}，人员数：{PersonnelCount}，预筛选缓存：{CacheSize}\\\", \\n                    context.Tasks.Count, context.AvailablePersonnel.Count, context.PrefilterResults.Count);\\n\\n                // 第一步：创建高性能求解�?\\n                using var solver = CreateAdvancedSolver();\\n\\n                // 第二步：构建决策变量矩阵 (x[i,j] = 1表示任务i分配给人员j)\\n                var decisionVariables = BuildDecisionVariables(solver, context);\\n                _logger.LogDebug(\\\"📊 决策变量构建完成 - 变量数量：{VariableCount}\\\", decisionVariables.Count);\\n\\n                // 第三步：构建辅助变量 (支持复杂约束建模)\\n                var auxiliaryVariables = BuildAuxiliaryVariables(solver, context, decisionVariables);\\n                _logger.LogDebug(\\\"🔧 辅助变量构建完成 - 辅助变量数量：{AuxCount}\\\", auxiliaryVariables.Count);\\n\\n                // 第四步：Level 1 基础约束 - 对应遗传算法的基础约束验证\\n                await AddLevel1BasicConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第五步：Level 2 组合约束 - 对应遗传算法的组合约束验�? \\n                await AddLevel2CombinationConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第六步：Level 3 业务约束 - 对应遗传算法的业务逻辑约束\\n                await AddLevel3BusinessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第七步：公平性约�?- 对应遗传算法的负载均衡机�?\\n                AddFairnessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第八步：设置多维度目标函�?- 对应遗传算法的适应度函�?\\n                SetEnhancedObjectiveFunction(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第九步：执行求解\\n                _logger.LogInformation(\\\"🔍 开始求解增强版线性规划模�?..\\\");\\n            \\n[... omitted 2156 of 2412 lines ...]\\n\\n                \\n                // 检查历史任务人员的分配\\n                if (context.PersonnelHistoryTasks.TryGetValue(personnelId, out var historyTask))\\n                {\\n                    var taskDate = task.WorkOrderDate.Date;\\n\\n                    if (historyTask.Any(m => m.WorkDate.Date == taskDate))\\n                    {\\n                        return true;\\n                    }\\n                }\\n                \\n                return false; // 人员可用\\n            }\\n            catch (Exception ex)\\n            {\\n                _logger.LogWarning(ex, \\\"⚠️ 检查人员不可用性异�?- 任务{TaskId}人员{PersonnelId}，默认为不可�?, \\n                    task.Id, personnelId);\\n                return true; // 异常情况下保守处理，标记为不可用\\n            }\\n        }\\n\\n        /// <summary>\\n        /// 【Ultra think方法】检查人员在指定任务时间是否不可用（异步版本�?\\n        /// 【扩展支持】：为未来集成更复杂的不可用性检查逻辑预留接口\\n        /// </summary>\\n        private async Task<bool> IsPersonnelUnavailableForTaskAsync(WorkOrderEntity task, long personnelId, GlobalAllocationContext context)\\n        {\\n            // 目前直接调用同步版本，保持异步接口以便未来扩�?\\n            await Task.CompletedTask;\\n            return IsPersonnelUnavailableForTask(task, personnelId, context);\\n        }\\n\\n        #endregion\\n    }\\n\\n    /// <summary>\\n    /// 人员任务矩阵 - 支持高效的关联查�?\\n    /// </summary>\\n    public class PersonnelTaskMatrix\\n    {\\n        public Dictionary<long, List<WorkOrderEntity>> PersonnelTaskMap { get; set; } = new();\\n        public Dictionary<long, List<GlobalPersonnelInfo>> TaskPersonnelMap { get; set; } = new();\\n    }\\n\\n    /// <summary>\\n    /// 时间窗口定义\\n    /// </summary>\\n    public class TimeWindow : IEquatable<TimeWindow>\\n    {\\n        public DateTime Date { get; set; }\\n        public long ShiftId { get; set; }\\n        public TimeSpan StartTime { get; set; }\\n\\n        public bool Equals(TimeWindow other)\\n        {\\n            if (other == null) return false;\\n            return Date.Date == other.Date.Date && \\n                   ShiftId == other.ShiftId && \\n                   StartTime == other.StartTime;\\n        }\\n\\n        public override bool Equals(object obj) => Equals(obj as TimeWindow);\\n        \\n        public override int GetHashCode() => HashCode.Combine(Date.Date, ShiftId, StartTime);\\n    }\\n\\n    /// <summary>\\n    /// 冲突严重程度枚举\\n    /// </summary>\\n    public enum ConflictSeverity\\n    {\\n        None = 0,        // 无冲�?\\n        Low = 1,         // 轻微冲突\\n        Medium = 2,      // 中等冲突\\n        High = 3,        // 严重冲突\\n        Critical = 4     // 关键冲突\\n    }\\n\\n    /// <summary>\\n    /// WorkOrderEntity扩展方法 - 为LinearProgrammingEngine提供辅助功能\\n    /// </summary>\\n    public static class WorkOrderEntityExtensions\\n    {\\n        /// <summary>\\n        /// 【深度实现】为验证目的克隆WorkOrderEntity\\n        /// 【业务思考】：在线性规划验证过程中，需要模拟分配的临时任务对象\\n        /// 【技术要点】：仅复制验证所需的关键字段，避免整体对象克隆的性能开销\\n        /// </summary>\\n        public static WorkOrderEntity CloneForValidation(this WorkOrderEntity original)\\n        {\\n            return new WorkOrderEntity\\n            {\\n                Id = original.Id,\\n                ProjectNumber = original.ProjectNumber,\\n                ProjectCategory = original.ProjectCategory,\\n                ShiftId = original.ShiftId,\\n                ShiftCode = original.ShiftCode,\\n                ShiftName = original.ShiftName,\\n                WorkOrderCode = original.WorkOrderCode,\\n                WorkOrderDate = original.WorkOrderDate,\\n                Priority = original.Priority,\\n                Urgency = original.Urgency,\\n                ComplexityLevel = original.ComplexityLevel,\\n                EstimatedHours = original.EstimatedHours,\\n                Status = original.Status,\\n                AssignedPersonnelId = original.AssignedPersonnelId, // 这个将被修改为模拟分配的人员ID\\n                // 关联对象也需要复�?\\n                ShiftEntity = original.ShiftEntity,\\n                ProcessEntity = original.ProcessEntity,\\n                WorkOrderFLPersonnels = original.WorkOrderFLPersonnels\\n            };\\n        }\\n    }\\n\\n    /// <summary>\\n    /// 解质量指�?\\n    /// </summary>\\n    public class SolutionQualityMetrics\\n    {\\n        public double TaskCompletenessRate { get; set; }\\n        public double TimeConflictRate { get; set; }\\n        public double LoadBalanceScore { get; set; }\\n        public double PersonnelUtilizationRate { get; set; }\\n        public double ConstraintSatisfactionRate { get; set; }\\n        public List<string> ViolationDetails { get; set; } = new();\\n    }\\n}\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:06:21.225Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:27.075Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing analysis plan for large file**\n\nThis file is huge, so I'll focus on breaking down the LinearProgrammingEngine by reviewing its structure first, then pinpointing its core responsibilities, and finally evaluating any issues or improvement opportunities. I think setting a clear plan with these steps will help me provide a solid, organized analysis summary."}}
{"timestamp":"2025-09-22T13:06:27.651Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":13226,"cached_input_tokens":9728,"output_tokens":662,"reasoning_output_tokens":448,"total_tokens":13888},"last_token_usage":{"input_tokens":6268,"cached_input_tokens":3712,"output_tokens":219,"reasoning_output_tokens":128,"total_tokens":6487},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:27.651Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Preparing analysis plan for large file**\n\nThis file is huge, so I'll focus on breaking down the LinearProgrammingEngine by reviewing its structure first, then pinpointing its core responsibilities, and finally evaluating any issues or improvement opportunities. I think setting a clear plan with these steps will help me provide a solid, organized analysis summary."}],"content":null,"encrypted_content":"gAAAAABo0UnUKnlYgo2eEspc4xOrn66xbitoxsU8D6Yi3n2JreQ-et8JijdQJHUhBGNmF3OuUYXoOsVJDfyeH99gWlFRfjKPRJ5SHH2wXu28eAczRXriYrVi9rV0mzwLDvvsrltPyDKS_7XRB3zmR6az_FLzn6hkiayLZwBrRs5IeDhEdn8-CeO9bfg_qTkkjqiBWf1rBrUok0-iLoAL-zSB9FW21gHe1aX4HftOqtu-vD-yL99NkCcyUQHV5pvTWUzllhylqa-WGMaPUu7BASM-AvvFiZVUDGoLsFBmO1x7XZ9tupxlfe0RC0IgYMnA4Ss0WV26qq8NNyuj4TkkfQLbuxh949wVSpxCSOrK5WLikvh6mMx67DcvCfI8ed4FCy2jNkeQxGMB0acY0eMATzb62EtisviEQp7H5r_20FwxTvJNSEP911ZNeXFxd-TvWv3ICSx5PxjwUUamL920XyHAyRfDwNFpEuMnOW8G1JIojPG0IeqrLgcnXJXRcyoioY0eNKi_zUllMlj1Nyd1cL-MOzRodwWueymRsIlXMcpDTn686ZWHJQ-XUoWpP6VfHdVw5xPomrycYCt4__5-1wHJ0kj_x9F1x5S12L1QJmP8wnotUDuudsi3w3XG_xUgxa-kNlS5qse3Fa2wl0rY712mJyVQbUuVM30ac_66F_-XrTvaVEalXGZCuFWtV4N_0Dh3qjv_KBZ5CUZpgUadT487xLz356k3xMb1pzkPYwDUjAFzaaoIc0j6qwKkljnOw5K_CBJ5Mxco1xfnumKTgRl7RjEGOCEA0wrgf4pe2nffEjYbDKf0fvjhakW7UfALnf5W4vxloz28TbMHhP0opV2CAvmT2VgzBWjJNXyPC9_SD2dY5PBTM4zrSWDSdyhuwCFHvLytqlWLmdRHl7Q9orx2_fsRgYuMlv1bsd_vOAaRDAOfcjjs6rHWtgRNhqtnRmlniUHqUyDa4mkSNpwRVPYIAwij3Pr0it665a3Ngft0XHG_czECXn_js00poW1tp5TJGokwuwGk7105gf0rInmfm83Dh2EjSuKg9GT3StTR_2MxB-A27XRn8c2VhYqLu2mAaQotVK9afmU374zNPA_VYyD-R5Qwadn36YT58KqoqkSUikIcfz3ypa6w8OhaLsg6rOMWjX_TDcMY3lhK-Rs9KtJ1fy69epS0mZ8nzcAwTAvfH4Pj5X_YAYR7v5n1rx7kbMdQuWNh6jY0ZSOgB7X8it0ktTmHcDTFixm1cKwi1OHc7Zfm9LZYoBnU4QeS4FFRgOfI9zVLeuGoifdMphVBKZALPUo6gdW10DfzkowB6-rec4eICz9a_3dYyBvVO4DWeIogsdm7XI58uwePosUSNlwYizea9FnJl5TrnBuAycl4pgqGnMBfZYHDKIqsTfvNdKMb_zCdDgCsN8HtgukODSJUT_833vy8ETf3rg7LiMWoP5Y3q1qdV84EMClFt7uTMXV-HWTdkMOhGcFWQOOHgWotKSd0oC63QJK1sR5xL3LHKUntstXZIxgUe0RzAFTAhZn14nOzwIKuTGWwMdRqeSeKJzKBfPtn566rLDyLwdp1NZRNg4pze3zctM53INNHHf3eLTVdiE7b_UBi7LBds6u6dGI7zZkxuJEmyvJTo07Ip91mfqtfNDI1Wtj0ClRQTKg2-4ak80BxixqT5HfpRqpNafhbGzVDUaqqFp8-iNzhm56vX4aEAhJ_pFKYha0c7aOaxnRySs445iwBR2EACk4ULVSuhMpK0TwBXGduADmwOouuvtx3wt_loK6FrwNPg2m8LlzR4oal_1uWZM4SgzCWNv35ci-vpz7jUGTwJIda5Dto5Y46aQ9PDVktyxW5lANaB8RV8JMDynwToGf6KBQgKc4V7YnZix_bz0K26S0zUSJ4IaXUboSICe2mIj8aiUnLezAT8X7XZRAkufZLth8RdIHor2nge1nGc19pu5gpgjGwntl7iZFwJ8JCQfIoKnhZsl_7lMsZfAzm-y3nRfhqSOAez0jPTw_PHQoc_5gAofaeGnlmJdG5Coq9zewHByIDJ5PTg9ISy91BVCR9IkFnGJlILQ=="}}
{"timestamp":"2025-09-22T13:06:27.651Z","type":"response_item","payload":{"type":"function_call","name":"update_plan","arguments":"{\"explanation\":\"Need a structured pass through the very large engine to summarize behavior and risks.\",\"plan\":[{\"status\":\"in_progress\",\"step\":\"Skim class layout to map major responsibilities and helpers\"},{\"status\":\"pending\",\"step\":\"Review critical methods for modeling, constraint setup, and objective\"},{\"status\":\"pending\",\"step\":\"Summarize findings, risks, and improvement ideas\"}]}","call_id":"call_YDfhYYQReMgutGma4TgIe32m"}}
{"timestamp":"2025-09-22T13:06:27.652Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_YDfhYYQReMgutGma4TgIe32m","output":"Plan updated"}}
{"timestamp":"2025-09-22T13:06:27.652Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:33.158Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Reviewing class structure overview**"}}
{"timestamp":"2025-09-22T13:06:33.863Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":19776,"cached_input_tokens":16128,"output_tokens":930,"reasoning_output_tokens":640,"total_tokens":20706},"last_token_usage":{"input_tokens":6550,"cached_input_tokens":6400,"output_tokens":268,"reasoning_output_tokens":192,"total_tokens":6818},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:33.863Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Reviewing class structure overview**"}],"content":null,"encrypted_content":"gAAAAABo0UnaBiIV4H3HcmmkUsImR8GXGzn4ofqcJXPO28Mpa48gXdI9wWquqTteUT-50ikOWNlcTqPJ3cGgh_YNb-Pg-Iz237V1er7Pslj5RkqLLuz0uYR4_iEiFI4f6rbujYkkELoBt-Nxb1OEnOfGzM_SmhRaG3rb1Y2KkE0XrL11qc0aRyRWyYOLUqtP468zKiWJo83vP0MoMrD_SNXPxQd_ndTCtzVou6dfux5B8TUHlZYFSi741CgnsBLny8JWOJ8UPd5DXyWOTzZevSOpy79k0sQ_7ra8VDMYo59wcDQGSW4o23009k7ulFD63cfiQrvBerjnwSS63ucJxQS_dS37QzqBK41sGf0dPElajBqjrLC0dxcEL13ZDsiVwiEWYiWWY89C2vybawyHTYC29RDIdm0kGjEvYv8mmY8GxUI5-LGa_1ba0hYgoODGGVwqgBm8LbeE-Rbk_yFm9zOgMbom9DV4R5hvawuGFJ_PRcb-d2ArqkrJznP0f34EABruO1WIaBYe46XaydBX2eNd9aqH3tBIcoqsjmsrFPEQXiCkEFMYCx4vSAHQbrCiJ-M5Q9IWiLwQKScaVogJeqS75uBnkhsh9QSZIIz83jl9x91gD97jlorvqiIPHPCbeZgwBudnm_DofQ5rwFjYe-QtjJ7qDph8Q2i7X4uoqM5S_QRYE2LgWXxGmb9qUTeDePgyWRiM2XMjwMjoO3J_LEoeVyoZbrYgmjTdSdA6i54YBo1c9A9x8aKugdofj1sVercrx8gyoWIYF6saU4WM0XZaDwOdWy_knxcqyZT7KAs_SyAlvSIFygCkjpNxA-kxHQqZrr_F3zI-AxQhQ7AlvMKbAoYWzqjCpRzgv5IefiAA9r7o2CdY0l1ZdCU7AR8QK6jkAhwByLLKHtGHv5qNHBiue3z5dNc8PjWN1rbkkgxgqCdfhd98fMEMm-6WxBXqEtKc-9UBBJEZHak66DhIVwjvreOPcWAFSe4WGUDo6QQA8gs9W8PY03Xy1sTfxTqBh6LAipGWE76q56Y3TrhVswT5YVluWBzDh1kMWdWJzcwVN_N2dgWWAnRZJjK9UVaZ15o4IG3SPhOmGoXTR9hsQcoQ0wBzNBdS2Poogr7o2NDq-mccnFEoazBy4l_S3WTjvyNTRxNdIBxJnc5xITIvn80ST9M8qfFxsB-djapjaRoTY0nCU_6nFPS9cgv2UGKh_Pc_pSC3LbZvvxzcm4fv-cUTHnwZUsnUof5Igv2VRaerurrsga8TTBySnMCReD-Hlvr6LwqA-N9JSv5ipROfzNctfLHBiljgqoLaTfO1nOxXzbpGAaRt_2yAB9TjctqoT33xfp-FLCyaLN7DOYC3-CsBjHCYVJUi2G3evWHoy6Utp08JcNnFPrbMTJsmgCxvahHagV7W0I5-ul03E9QabsOtJe_g4VITdl-HIjirbVIFE6vQBHqRRnkZpL-hUjIAO_x-1TTRuq-93-3TP-RJ2xJCXthsKkJmigdDb59V5l6gOond0rWeX73YdbmewsKcsjJNEdK7FBV5QUNHrkHkwU2BvmByrXoAoNLg0OFKULIeeMlJdpTGI9DX83Og0eL6nBtD26HWPSWOBf1aruUy968ToHK8x93lwuar1j64XLuZv-TQ50ObBTzAozWtfL4RCnEzeSAWNz1FIVxMc07HSYlfSqr7xY9ZgM0tjNNJ0T7Tl0GJRoAtXjO9GcmZescCpCDdGL176NgMhRrlk5pFNcblxW9sKS1dl66Yg77v2Fsxrk3QFrS02_1cjCjdvLkGKRhWQGe964zWj-chHdNIdwaGSlGvP_QddjkAjb8ttaW7BOMytAKvg2ykMJV0so3bUuiZyp2nhmJXg9bmBRroyueT9b6rBOfswnVKY5lTHjFK-x-Z6K1q2AVz3q7kdDG-zP81z6x6UIDSG2rCH2vP4HDF0JS9YP7vBiDLoOZLyINCExESM8v9IPiKJQSIGyM0ZDWco_hmMstLZPYAeevpdm0LpVt83F_OfA=="}}
{"timestamp":"2025-09-22T13:06:33.864Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -First 200\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_Fpn6WbUaLpI2T0bO5TD0r17Z"}}
{"timestamp":"2025-09-22T13:06:33.864Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Fpn6WbUaLpI2T0bO5TD0r17Z","output":"{\"output\":\"using System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing System.Threading.Tasks;\\nusing Google.OrTools.LinearSolver;\\nusing Microsoft.Extensions.Logging;\\nusing NPP.SmartSchedue.Api.Contracts.Domain.Work;\\nusing NPP.SmartSchedue.Api.Contracts.Domain.Personnel;\\nusing NPP.SmartSchedue.Api.Contracts.Services.Integration.Internal;\\nusing NPP.SmartSchedue.Api.Contracts.Services.Integration.Output;\\n\\nnamespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\n{\\n    /// <summary>\\n    /// 增强版线性规划引�?- 实现遗传算法中的所有复杂约�?\\n    /// 核心功能：三级约束体�?+ 多维度目标优�?+ 完整业务规则支持\\n    /// 设计理念：将遗传算法的启发式约束转换为严格的数学约束，实现精确求�?\\n    /// 技术架构：Google OR-Tools + 分层约束建模 + 自适应权重优化\\n    /// 业务价值：提供数学最优解，替代遗传算法的近似解，提升调度质量\\n    /// </summary>\\n    public class LinearProgrammingEngine\\n    {\\n        private readonly ILogger<LinearProgrammingEngine> _logger;\\n        private readonly ShiftRuleValidationEngine _shiftRuleEngine;\\n        \\n        // 求解器配�?- 增强版配置支持复杂约�?\\n        private const string SOLVER_TYPE = \\\"CBC_MIXED_INTEGER_PROGRAMMING\\\";\\n        private const double OPTIMALITY_TOLERANCE = 1e-6;\\n        private const int MAX_SOLVER_TIME_SECONDS = 600; // 增加�?0分钟支持复杂约束\\n        private const int MAX_CONSTRAINT_COUNT = 50000;   // 最大约束数量限�?\\n        \\n        // 约束权重配置 - 对应遗传算法的三级约束体�?\\n        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 基础约束严厉惩罚\\n        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 组合约束重度惩罚  \\n        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 业务约束中度惩罚\\n        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 公平性奖励系�?\\n\\n        public LinearProgrammingEngine(\\n            ILogger<LinearProgrammingEngine> logger,\\n            ShiftRuleValidationEngine shiftRuleEngine)\\n        {\\n            _logger = logger ?? throw new ArgumentNullException(nameof(logger));\\n            _shiftRuleEngine = shiftRuleEngine ?? throw new ArgumentNullException(nameof(shiftRuleEngine));\\n        }\\n\\n        /// <summary>\\n        /// 执行增强版线性规划优�?- 实现遗传算法等价的约束体�?\\n        /// 业务流程：构建决策变�?�?三级约束建模 �?多目标优�?�?精确求解 �?解析结果\\n        /// 核心创新：将启发式约束转换为线性约束，实现数学最优解\\n        /// </summary>\\n        public async Task<GlobalOptimizedSolution> OptimizeAsync(GlobalAllocationContext context)\\n        {\\n            var stopwatch = System.Diagnostics.Stopwatch.StartNew();\\n            \\n            try\\n            {\\n                _logger.LogDebug(\\\"🚀 启动增强版线性规划优�?- 任务数：{TaskCount}，人员数：{PersonnelCount}，预筛选缓存：{CacheSize}\\\", \\n                    context.Tasks.Count, context.AvailablePersonnel.Count, context.PrefilterResults.Count);\\n\\n                // 第一步：创建高性能求解�?\\n                using var solver = CreateAdvancedSolver();\\n\\n                // 第二步：构建决策变量矩阵 (x[i,j] = 1表示任务i分配给人员j)\\n                var decisionVariables = BuildDecisionVariables(solver, context);\\n                _logger.LogDebug(\\\"📊 决策变量构建完成 - 变量数量：{VariableCount}\\\", decisionVariables.Count);\\n\\n                // 第三步：构建辅助变量 (支持复杂约束建模)\\n                var auxiliaryVariables = BuildAuxiliaryVariables(solver, context, decisionVariables);\\n                _logger.LogDebug(\\\"🔧 辅助变量构建完成 - 辅助变量数量：{AuxCount}\\\", auxiliaryVariables.Count);\\n\\n                // 第四步：Level 1 基础约束 - 对应遗传算法的基础约束验证\\n                await AddLevel1BasicConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第五步：Level 2 组合约束 - 对应遗传算法的组合约束验�? \\n                await AddLevel2CombinationConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第六步：Level 3 业务约束 - 对应遗传算法的业务逻辑约束\\n                await AddLevel3BusinessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第七步：公平性约�?- 对应遗传算法的负载均衡机�?\\n                AddFairnessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第八步：设置多维度目标函�?- 对应遗传算法的适应度函�?\\n                SetEnhancedObjectiveFunction(solver, decisionVariables, auxiliaryVariables, context);\\n\\n                // 第九步：执行求解\\n                _logger.LogInformation(\\\"🔍 开始求解增强版线性规划模�?..\\\");\\n            \\n[... omitted 0 of 200 lines ...]\\n\\n            );\\n\\n            _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\n            return solver;\\n        }\\n\\n        /// <summary>\\n        /// 【Ultra think增强】构建决策变�?- 集成人员班次不可用性的智能变量生成\\n        /// 【核心优化】：在变量创建阶段就排除不可用组合，大幅提升求解效率\\n        /// 【三层优化】：预筛选结�?+ 不可用性检�?+ 评分阈值，实现精确的变量裁�?\\n        /// 【性能提升】：预计减少20-40%的决策变量数量，显著提升求解速度\\n        /// </summary>\\n        private Dictionary<(long TaskId, long PersonnelId), Variable> BuildDecisionVariables(\\n            Solver solver, GlobalAllocationContext context)\\n        {\\n            var variables = new Dictionary<(long TaskId, long PersonnelId), Variable>();\\n            var createdCount = 0;\\n            var skippedCount = 0;\\n            var unavailabilitySkippedCount = 0;\\n            \\n            foreach (var task in context.Tasks)\\n            {\\n                foreach (var personnel in context.AvailablePersonnel)\\n                {\\n                    var prefilterKey = $\\\"{task.Id}_{personnel.Id}\\\";\\n                    \\n                    // 【Ultra think优化1】首先检查人员班次不可用�?- 硬排�?\\n                    if (IsPersonnelUnavailableForTask(task, personnel.Id, context))\\n                    {\\n                        unavailabilitySkippedCount++;\\n                        skippedCount++;\\n                        _logger.LogDebug(\\\"🚫 跳过不可用组�?- 任务{TaskId}人员{PersonnelId}在{Date}班次{ShiftId}不可�?, \\n                            task.Id, personnel.Id, task.WorkOrderDate.ToString(\\\"yyyy-MM-dd\\\"), task.ShiftId);\\n                        continue;\\n                    }\\n                    \\n                    // 【Ultra think优化2】基于预筛选结果的智能决策\\n                    if (context.PrefilterResults.TryGetValue(prefilterKey, out var prefilterResult))\\n                    {\\n                        // 对于评分较高的分配，即使标记为不可行也创建变量（可能是保守评估）\\n                        if (prefilterResult.IsFeasible || prefilterResult.PrefilterScore > 30.0)\\n                        {\\n                            var variableName = $\\\"x_{task.Id}_{personnel.Id}\\\";\\n                            var variable = solver.MakeBoolVar(variableName);\\n                            variables[(task.Id, personnel.Id)] = variable;\\n                            createdCount++;\\n                        }\\n                        else\\n                        {\\n                            skippedCount++;\\n                        }\\n                    }\\n                    else\\n                    {\\n                        // 【Ultra think优化3】无预筛选数据时的保守策�?\\n                        // 即使无预筛选数据，但已通过不可用性检查，仍创建变�?\\n                        var variableName = $\\\"x_{task.Id}_{personnel.Id}\\\";\\n                        var variable = solver.MakeBoolVar(variableName);\\n                        variables[(task.Id, personnel.Id)] = variable;\\n                        createdCount++;\\n                    }\\n                }\\n            }\\n\\n            _logger.LogDebug(\\\"📊 【增强版】决策变量构建统�?- 创建：{Created}个，跳过：{Skipped}个（不可用性：{UnavailabilitySkipped}），变量密度：{Density:P2}，不可用性优化率：{UnavailabilityRate:P2}\\\", \\n                createdCount, skippedCount, unavailabilitySkippedCount, \\n                (double)createdCount / (createdCount + skippedCount),\\n                (double)unavailabilitySkippedCount / (createdCount + skippedCount));\\n\\n            return variables;\\n        }\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:06:33.864Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:37.210Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Searching for code regions and headings**"}}
{"timestamp":"2025-09-22T13:06:38.048Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":28776,"cached_input_tokens":22784,"output_tokens":1062,"reasoning_output_tokens":704,"total_tokens":29838},"last_token_usage":{"input_tokens":9000,"cached_input_tokens":6656,"output_tokens":132,"reasoning_output_tokens":64,"total_tokens":9132},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:38.048Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Searching for code regions and headings**"}],"content":null,"encrypted_content":"gAAAAABo0UnebqH4rdJmeHf5YCQ9vPoleTNuYe3GiXy3zEyNZdu0v1ufpbwTxhaekFHckzPj5CZ2qbvQcq3lzU-aehU1xaDHkQzQAR8Pm_PGfgQEfGO9sEWWiQFi8y_OXEmvViEsU5fHKQRShZaolL9TayIkKFX6NYVVD5MfjF7sqqQ9a6X7OmcQdT96POtnnN_kMxEQ25g_ufTQIH0Ke6I1WznzB4SY50CVsRGz4Y5ioiKSGKshAFnLcKPxpqxEg-OVnlLluL7Yh8P6R8Xmu0Tpk3pgrNd7qOzJtKj2fQQB0mfhLtlf3YalDGMB7iEsbO8QBwpkbdrCsAoq8Iwzc4ICzvmNOzLv-Q0Va-QtUq8pFKxv26q-ZIY2sXAg1MG0xLfqSAJV71u9zWS8bypXs2loVdsgbMe-EpGs7JQgGmwEITy_cxQdWO-lydDTdXndd0TF0pILXBmJT6sxmDsn1UVavJ293Se7NhR4tNXQ_S0Cq7YLag_wKq0P6wOpjGh8xu0XEihTtW7G6WNBG6uxR20fRe8XNSP-CD11o42QTT72aZMPSOffGHSGfy8JWPmomekuRLMp_sl3MDONm9ziJR-dW5MMt30F1632ZLWbjSzOS2YCrl4a8jTzhs5GjCvciAzMo7VrvTldpSMgC5ekLEM7hrhONLhPK3j0D-CzWKmYFxVbXJoxAS1-Z5uVkYCakIgtPYodNb9f48UByLf2BbwRr9WaFeDdwk3lhHyikke8fmrK0LHwVdBztCLeBp3YQsuDIYAP-uZViWDhshsEwyBiMvSCYqKXbqtqpEvus9nKrOzFZ1TqN69I0M0Tiga5sI5xLF8voxcMBICA2awlU1A6J9tsds4z114v6ltcmwb3lTp0YLVYiUvsmIwOT9ExL0K5H_ZX0vfpULiD3nTCHKQodHk-IQyqNfE4sYZDhXCKWoIMsvRuO2MBtbL6bdioOKAS3qElcwtNYNYlYFGAI7HqyIOU9mo6yuLWlNrR38SPmH5DwM2atUTwZmI_xuMOdhAr-dU3t7yalLe-E9njwdk-pwuLJDTilSCMIk8_UTGWJrMps1mqKDOCss15pQPqgFac3XSuxZEsF-ZJQ54HouRL7X-1CGD_6nwvJ6jExtrPB5_c0bDjYf4L-RnglwfTcTlB0s1aXqv4CvccrK7_3E-QjIX0oq-fo5eOjtub6wohk0rkjysMxEjXFmYE7VQrEtY4RAup7R-Jj22lzsQncOOFnHBsDPK9ow=="}}
{"timestamp":"2025-09-22T13:06:38.048Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"rg \\\"#region\\\" NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_hCtzYgQRbbKjvdowvb5tUogq"}}
{"timestamp":"2025-09-22T13:06:38.048Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_hCtzYgQRbbKjvdowvb5tUogq","output":"{\"output\":\"        #region 求解器和变量构建\\r\\n        #region Level 1 基础约束 - 对应遗传算法基础约束验证\\r\\n        #region Level 2 组合约束 - 对应遗传算法组合约束验证\\r\\n        #region Level 3 业务约束 - 对应遗传算法业务逻辑验证\\r\\n        #region 公平性约束 - 对应遗传算法负载均衡机制\\r\\n        #region 目标函数 - 对应遗传算法适应度函数\\r\\n        #region 结果处理和验证\\r\\n        #region ShiftRuleValidationEngine集成辅助方法\\r\\n        #region 辅助方法\\r\\n        #region 增强版批次感知验证支持方法\\r\\n        #region 人员不可用性检查辅助方法\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.2}}"}}
{"timestamp":"2025-09-22T13:06:38.049Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:43.677Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Analyzing large file structure**"}}
{"timestamp":"2025-09-22T13:06:44.648Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":38118,"cached_input_tokens":31744,"output_tokens":1336,"reasoning_output_tokens":896,"total_tokens":39454},"last_token_usage":{"input_tokens":9342,"cached_input_tokens":8960,"output_tokens":274,"reasoning_output_tokens":192,"total_tokens":9616},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:44.648Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Analyzing large file structure**"}],"content":null,"encrypted_content":"gAAAAABo0UnkMfa9Q_kmUFpR3B3ZT5J8_JwBrvNlflDJeYwa0XnUxAkGAXlHq4z3yxWA4Oi4oK_MqJ-yIt8_RsOVj5kU8bjJHInbtqxeqlhLPCTqztxCeLruNggmtQXCtNNa2eKbZ8ShT5GoXPM22zHcVFj8Ncd7qAK5fuj9ODov5pFd3WYCZlqr0kMs8PgCRUahlGkTKWug-AO-fR9jrq909SXY3Db2nzCwJMo5lOQVvP_eyyef_rld3W27cJyZVn4jUKisV7oyvWrjmRAwt4cFTCGwFA80IgA0yvKLPLtDtBfQSZbFSsiWkvNGg0YuWE4JF1b9TL3wRIPNt2TqaeCTkxi0nFdLQerdXJP9uFhgA3728yVhHJCfktMDnGzu7dwWaRHAttxJkFEkIeoukIqO7pPWfo4qsF-NP9rayXv46ZgPLDZovNy2sKeMVPvms-NcM-RhQ1zxa6cM2wQScUJMK9e6GwblUjmz30GT1P5xZid7YItdHU6hWdds4etyEQLvdzXlD_Yn5aTVoIbHz9aK93WCQr6_y8DwNWZUxI1i9K988I_ihBYBra9vpT1YMh2YCn0jIlFBRxbiLaIY05a7CJm-VA19lq-y66fXGgkhXp6f2FRAE6wCIPb3UN-3YBVLKbfmKbUzweGGjYCitBnf-oJbaO9qyN63XOT-yCoxdhS7-Oibls80fGcAgYSTz6pZzWlRFazlGmqdC9VXRjjCWAade7o2vXdrcyn6cgBXZVkzUzkIZjn4KdsPub0ck8wZoncOdEvvMPgwq5G3u1TjlsQaNl6mossTnweFFrnt11Am5gK5TnPvikj2k1CCeMsUKcpTDxeB4bYX4OLtiVQHnx7ZUNgq2YMNLGSXooEvpqCeBmfmUgPTTj8RkEtvBpCQMOEUsfX_NDi1k_Qz99hY7cwh7JcDpV0Z09BdRt1x2kLjxLYmCY64-yAOruZf__uV5NnqAYeUcH_W_1e99T8g7Y5sD7xtjLOMZvx6sccSsbiB57o3CAKZQiFklfNW8lXmeFBns0AOH2bixEWV300-icZxlQMaOm2IwuG26wj2FyoaTwhQtk6PKwBIoowz6DbWXXq8a-8oEkO50cXDq6eMgAHGjbTXEZx0dv-2P2nq-UNIi1pCT2m7kFsXHi994Mg5GiSPVchfYR66sZ1W_wyCYd1h5VO2IhXNIJKmU7LZ3VmPCx6VzW7Omtzl6PZb5bj6sCoEC-ar7BPR_CjLoWwLa51hUPBNuC1sbYLW0vKm2u4-pWWHheHK_wMAZMREsbHmtJryfHmEZ2Jv0V8zJShDKjEBXKQsmdzi6vOGJiegtkkC7Parrt1_kGKXDfRszFwc_q0ZGyCWQ_xPN3eiu5LUKNMjdEbwnsOmnxsJD2U8tdjq847YroaAMQatcVyKFz_WMjiZC1ww0QuJ8-hDS-kQgz_d6FS6tw0kDZDV2Ui5Tf-nuGYZUEcbOKsvyA_L3I8hceVQzC3wzhXB_mmKBb2V9w5Y4-0ka6Da2sfk3yCrsHm9c3HA6H4s9nztFXqfcXrH_OODf_OtaieFcMbY8S7NwABjJZ1Kz7TKZmWQgnQHHovQMeraioOEWa2o_XDZ3SL9WSx1H8jS1g1WPnJfEshs-BNYWDdTooaKrayVGYzReLPZYNWYg6IlupZPQdA9Ea2mWGGqW5YP7qlFpziI-GYRhq5mfb1d2StQCis1QDBYVQHXmdZcp2YozdfkcTfqlPXQ57U94b1fqKTRfMKbLliFDe-kXYSa7vDIo7FFyJ8U0VvnpXnk3q67-pDzUDyike1dTMiuivirI5jo_Z0C4e-paCvc2BpCbBWNLgIEVwatcLF23Kl4ijcz3-o2QtWhTV-u9yAU7Hb_vR7DtzxBOQ5btKcNCB3X-KWTibF_NxgFDQrEKEN-lqo_Yk3PSV7me-0gEzRSmrSpm0mI27WeHr_rk_TGy5uFo0WYWNKoDk16PPlt5iTnNQTBU-jdA2vLI09kx8nOIYXsSsmt8sGguHIkl47Ckq6ZyiFtI4B6Ef9bYIMaAH207Y0YNNSEPtP3pwuSOLduu5SKjyPZcGLNnIxU_AQictRPPj2TEOlEEhMD9sSZXIenkFDSp2yErezSVcVBO6cT4Qjn"}}
{"timestamp":"2025-09-22T13:06:44.649Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"BuildAuxiliaryVariables\\\" -Context 0,40\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_CKwa6GHKAg3fXPcEj7OCYPvA"}}
{"timestamp":"2025-09-22T13:06:44.649Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_CKwa6GHKAg3fXPcEj7OCYPvA","output":"{\"output\":\"\\n> NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:68:                var auxiliaryVariables = BuildAuxiliaryVariables(solver, context, d\\necisionVariables);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:69:                _logger.LogDebug(\\\"?? ��������������� - ��������������{AuxCount}\\\", \\nauxiliaryVariables.Count);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:70:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:71:                // ���Ĳ���Level 1 ����Լ�� - ��Ӧ�Ŵ��㷨�Ļ���Լ����֤\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:72:                await AddLevel1BasicConstraints(solver, decisionVariables, auxiliar\\nyVariables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:73:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:74:                // ���岽��Level 2 ���Լ�� - ��Ӧ�Ŵ��㷨�����Լ����֤  \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:75:                await AddLevel2CombinationConstraints(solver, decisionVariables, au\\nxiliaryVariables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:76:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:77:                // ��������Level 3 ҵ��Լ�� - ��Ӧ�Ŵ��㷨��ҵ���߼�Լ��\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:78:                await AddLevel3BusinessConstraints(solver, decisionVariables, auxil\\niaryVariables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:79:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:80:                // ���߲�����ƽ��Լ�� - ��Ӧ�Ŵ��㷨�ĸ��ؾ������\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:81:                AddFairnessConstraints(solver, decisionVariables, auxiliaryVariable\\ns, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:82:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:83:                // �ڰ˲������ö�ά��Ŀ�꺯�� - ��Ӧ�Ŵ��㷨����Ӧ�Ⱥ���\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:84:                SetEnhancedObjectiveFunction(solver, decisionVariables, auxiliaryVa\\nriables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:85:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:86:                // �ھŲ���ִ�����\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:87:                _logger.LogInformation(\\\"?? ��ʼ�����ǿ�����Թ滮ģ��...\\\");\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:88:                var resultStatus = solver.Solve();\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:89:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:90:                stopwatch.Stop();\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:91:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:92:                // ��ʮ������������֤���\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:93:                var solution = await ProcessEnhancedSolverResult(solver, decisionVa\\nriables, auxiliaryVariables, \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:94:                    context, resultStatus, stopwatch.ElapsedMilliseconds);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:95:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:96:                return solution;\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:97:            }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:98:            catch (Exception ex)\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:99:            {\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:100:                stopwatch.Stop();\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:101:                _logger.LogError(ex, \\\"?? ��ǿ�����Թ滮�Ż��쳣����ʱ��{ElapsedMs}\\nms\\\", stopwatch\\n[... omitted 0 of 103 lines ...]\\n\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:06:44.649Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:06:50.308Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Investigating method definitions and async usage**"}}
{"timestamp":"2025-09-22T13:06:51.450Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":49032,"cached_input_tokens":41216,"output_tokens":1547,"reasoning_output_tokens":1024,"total_tokens":50579},"last_token_usage":{"input_tokens":10914,"cached_input_tokens":9472,"output_tokens":211,"reasoning_output_tokens":128,"total_tokens":11125},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:06:51.450Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Investigating method definitions and async usage**"}],"content":null,"encrypted_content":"gAAAAABo0UnrWjAV74fd0zFlZjPi4ioSZ8BGkalvI3gbYv4mWIZoyMsDIhPeFUdL74eR5QLIs1qK6azATmOUNmKQlnkJJactAo_0o8W5HB_Szb9QidVbtOyRstGQr8t19HlxeHGeVisdUWvyfnJsMIqOBuKTAFJrN8qcLzxF1ydr4LCpMaGrqv4DVxAogKT5r0hXF4it1GZk0UsKAq_KYkMI7reb6ZU697gKit8FA0VOltdxZAPlHd2lJEJPs9s2-U2J2WiqxuiSF43T2_C2NrwucB7sju74BMHoI0q9Mq8sj9d3OKM101g3knR4VBhtK2NHdKq8r-iAIOETDuiAopGKf8B9Uxj9arHXt900zVwhO_aosso-Q63GSFZFB6V-LZpYhReoemt40VD3XW9J6nJOMWXO8njVNeIC_sRewduJl7Gzh-sRBGTRvBFGlDTN3fYix12gJ4zb1zwXcnwpiUCYAE4D8aw8_YGp-HarzFl2iOUHKpHBd0Lc7_Dw0zuvhYrUeahiiGGDyrDPmc6usA4ip_Ltyvk6XIOyT71YPebbcMlCOD5qvJW7jw7_L56wUubVRQlnc-kYK4vPQLzWqrSJDMsbVn-fDb_ldRbXrmSjeb4Eek7aydtC0AsheYRwGJDfN97wVh1AMqcN7h4QfDfjgBXtEVmU-fdqXS5lSoYfPcZXgl7Y6dNKKF3u5hweQEn5vOHcCSfZWO-21J3p6fCCDkByrQ27btlM8HljnWOeWtOjFSsC7kxjCwzKAz5wu3b6iXb4l4LrQ9uUlTIsNkCQDjRsipCtKmSBeKz7SIf_Z6AvVBwcrgBoSwKX9dS-P2V-aWDiJiccjVJ9eusFgIie0h_XOpjGVXCQIdJvDYS55XXA0SPksOsCKW8ZZ29W7YQMb1IhqzmQe0Scfx6C2fB0udGQVzpI5EKmbSF3KTdrMsqHTS0_fGcs60DSex39fjcMfq6YhPSZ8Q3OL1pA9S7JmqFj6wctm34ufPmfouM8kQgSclysAJmwxnsFmuznqEBNC0_6JlfiUIDWZcohUR7nAwwPVcRNxU1GIANq3CUSmF8KbYJaIOnBaDSWnOVgwnWR2uDKoA_JpSxiwt9CerhYuYjWS8F_Q6zIDB1SbyyUGEKQ2fEY4dYoAXm7ihCmW8hOGoxLMDpieQwAVm-oU1oEJ-8ieGlwgB6SEFHBFC0m2OrtFZwVan1jz8u2BDAslQ7-VqRBz7JCpOA6CF0UMMzT7KY3QmQWkF3EikXTfKYcYecRskxJarr8xfT-_gCfbBRvVuD_kVxv-yEHOFTMa6zN6lTxfvclwrY06l28FWaYB26rmxElumrEz5-LYKqLypP73DGTKYcdPt1ckDPstg3kndITUiE8wOVWNKCAVGDaUHCqzPIISJAKJQLHrUT4FURlbZ2qQr_bTDDOw-kkj_At6IaXVb-WwrbytjHnWbP8KS0Rxj14eoM4vlc9mAhuH8sj7DIx8L3USyIE245bSwQiOwmYOH4jD1mkqje0sG8VecCASs49XNm7Jm9KEqxFfcvXWJ7ULpZpNIsavzTS8JoMEIxz6rYiZjHtuYTLiMaMF0BMQA2K1lPdPH8t0_4lZIDAly03S793XAtb-wcXq7P5rOM7c6anVZuIkBbWagu4V-n2ceNNkGe4WdhiOm5hkwTdoQu2H_gePWULT0K_0Dj6pOS5EBnD5p1VovCeqNvYjJpaM2u2vYb9lTL2H8ADFlmpjXyRRs3UKPni7pzu6sGzDhGXlUIp6zvbU4njYYjJgCCWutT0FZ65VI2ENryGpJwl3PrQJ1TrF3lfCEVsmhLasJxgSAeBr-Vu93mYZye3OIHBIr3T2hyrvRHiJTDkkwBEaBXc56qLY6DxJShBEREXzlEWC0S2TGoTCxtpafH0P7nAfB_MmxliOdpBbim19ompppM5hXCjLrEW4aTULs9Cpti5m3Iu-JLsCyOD93qXYe7PZxF1xmY-GMXgx8g30HJNu7j_PEK4eHJJU-y5QDBe2Lco1bJEcv3cab-U8ij8LQF1bfBGumhdbRupEwiNvVvm6OktgDWZ"}}
{"timestamp":"2025-09-22T13:06:51.450Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"AddLevel1BasicConstraints\\\" -Context 0,120\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_R2dy2yoVfBTT2IkvqYsNLI2h"}}
{"timestamp":"2025-09-22T13:06:51.450Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_R2dy2yoVfBTT2IkvqYsNLI2h","output":"{\"output\":\"\\n> NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:72:                await AddLevel1BasicConstraints(solver, decisionVariables, auxiliar\\nyVariables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:73:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:74:                // ���岽��Level 2 ���Լ�� - ��Ӧ�Ŵ��㷨�����Լ����֤  \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:75:                await AddLevel2CombinationConstraints(solver, decisionVariables, au\\nxiliaryVariables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:76:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:77:                // ��������Level 3 ҵ��Լ�� - ��Ӧ�Ŵ��㷨��ҵ���߼�Լ��\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:78:                await AddLevel3BusinessConstraints(solver, decisionVariables, auxil\\niaryVariables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:79:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:80:                // ���߲�����ƽ��Լ�� - ��Ӧ�Ŵ��㷨�ĸ��ؾ������\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:81:                AddFairnessConstraints(solver, decisionVariables, auxiliaryVariable\\ns, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:82:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:83:                // �ڰ˲������ö�ά��Ŀ�꺯�� - ��Ӧ�Ŵ��㷨����Ӧ�Ⱥ���\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:84:                SetEnhancedObjectiveFunction(solver, decisionVariables, auxiliaryVa\\nriables, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:85:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:86:                // �ھŲ���ִ�����\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:87:                _logger.LogInformation(\\\"?? ��ʼ�����ǿ�����Թ滮ģ��...\\\");\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:88:                var resultStatus = solver.Solve();\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:89:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:90:                stopwatch.Stop();\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:91:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:92:                // ��ʮ������������֤���\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:93:                var solution = await ProcessEnhancedSolverResult(solver, decisionVa\\nriables, auxiliaryVariables, \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:94:                    context, resultStatus, stopwatch.ElapsedMilliseconds);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:95:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:96:                return solution;\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:97:            }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:98:            catch (Exception ex)\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:99:            {\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:100:                stopwatch.Stop();\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:101:                _logger.LogError(ex, \\\"?? ��ǿ�����Թ滮�Ż��쳣����ʱ��{ElapsedMs}\\nms\\\", stopwatch.ElapsedMilliseconds);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:102:                \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:103:                return CreateFailureSolution(ex.Message, stopwatch.ElapsedMillisec\\nonds);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:104:            }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:105:        }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:106:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:107:        #region ������ͱ�������\\n  NPP.\\n[... omitted 26 of 282 lines ...]\\n\\nvices\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:362:                    {\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:363:                        constraint.SetCoefficient(kvp.Value, 1);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:364:                    }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:365:                    constraint.SetCoefficient(auxiliaryVars[$\\\"workload_{personnel.\\nId}\\\"], -1);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:366:                    constraintCount++;\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:367:                }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:368:            }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:369:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:370:            _logger.LogDebug(\\\"? Level 1����Լ��������� - Լ��������{ConstraintCou\\nnt}\\\", constraintCount);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:371:        }\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:372:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:373:        #endregion\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:374:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:375:        #region Level 2 ���Լ�� - ��Ӧ�Ŵ��㷨���Լ����֤\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:376:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:377:        /// <summary>\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:378:        /// ����Level 2���Լ�� - ��Ӧ�Ŵ��㷨��ExecuteLevel2CombinationConstraint\\nValidation\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:379:        /// Լ�����ݣ���̬��ι��򡢿�������Դ����Լ��\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:380:        /// Լ���ص㣺����������ϵĶ�̬��֤��Υ��ʱ�ضȳͷ�\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:381:        /// </summary>\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:382:        private async Task AddLevel2CombinationConstraints(\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:383:            Solver solver,\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:384:            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:385:            Dictionary<string, Variable> auxiliaryVars,\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:386:            GlobalAllocationContext context)\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:387:        {\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:388:            var constraintCount = 0;\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:389:            \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:390:            _logger.LogDebug(\\\"?? ��ʼ����Level 2���Լ��...\\\");\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:391:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:392:            // ���Լ��1����̬��ι���Լ�� - ��ӦCalculateDynamicShiftRuleScore\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:393:            //constraintCount += await AddDynamicShiftRuleConstraints(solver, deci\\nsionVars, auxiliaryVars, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:394:            \\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:395:            // ���Լ��3����Ա�չ���������Լ��\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:396:            //constraintCount += AddDailyWorkloadConstraints(solver, decisionVars,\\n auxiliaryVars, context);\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:397:\\n  NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:398:            _logger.LogDebug(\\\"? Level 2���Լ��������� - Լ��������{ConstraintCou\\nnt}\\\", constraintCount);\\n\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.4}}"}}
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{"timestamp":"2025-09-22T13:06:56.223Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Exploring method names and comments**"}}
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{"timestamp":"2025-09-22T13:06:57.255Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"rg \\\"^(\\\\s*)(public|private|protected)\\\" -n NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_xTZUbyQpUB4h1BOZGzPOxe9e"}}
{"timestamp":"2025-09-22T13:06:57.255Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_xTZUbyQpUB4h1BOZGzPOxe9e","output":"{\"output\":\"21:    public class LinearProgrammingEngine\\r\\n23:        private readonly ILogger<LinearProgrammingEngine> _logger;\\r\\n24:        private readonly ShiftRuleValidationEngine _shiftRuleEngine;\\r\\n27:        private const string SOLVER_TYPE = \\\"CBC_MIXED_INTEGER_PROGRAMMING\\\";\\r\\n28:        private const double OPTIMALITY_TOLERANCE = 1e-6;\\r\\n29:        private const int MAX_SOLVER_TIME_SECONDS = 600; // 增加到10分钟支持复杂约束\\r\\n30:        private const int MAX_CONSTRAINT_COUNT = 50000;   // 最大约束数量限制\\r\\n33:        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 基础约束严厉惩罚\\r\\n34:        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 组合约束重度惩罚  \\r\\n35:        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 业务约束中度惩罚\\r\\n36:        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 公平性奖励系数\\r\\n38:        public LinearProgrammingEngine(\\r\\n51:        public async Task<GlobalOptimizedSolution> OptimizeAsync(GlobalAllocationContext context)\\r\\n112:        private Solver CreateAdvancedSolver()\\r\\n141:        private Dictionary<(long TaskId, long PersonnelId), Variable> BuildDecisionVariables(\\r\\n205:        private Dictionary<string, Variable> BuildAuxiliaryVariables(\\r\\n278:        private async Task AddLevel1BasicConstraints(\\r\\n382:        private async Task AddLevel2CombinationConstraints(\\r\\n407:        private async Task<int> AddDynamicShiftRuleConstraints(\\r\\n531:        private int AddDailyWorkloadConstraints(\\r\\n576:        private async Task AddLevel3BusinessConstraints(\\r\\n609:        private int AddFLPriorityConstraints(\\r\\n671:        private int AddSkillMatchingConstraints(\\r\\n709:        private int AddProjectContinuityConstraints(\\r\\n774:        private async Task<int> AddQualificationConstraints(\\r\\n835:        private void AddFairnessConstraints(\\r\\n932:        private void SetEnhancedObjectiveFunction(\\r\\n961:        private void AddBaseAssignmentScore(\\r\\n995:        private void AddConstraintViolationPenalties(\\r\\n1022:        private void AddFairnessRewards(\\r\\n1038:        private void AddBusinessPriorityRewards(\\r\\n1087:        private async Task<GlobalOptimizedSolution> ProcessEnhancedSolverResult(\\r\\n1156:        private async Task<SolutionQualityMetrics> ValidateSolutionQuality(\\r\\n1219:        private void LogWorkloadAnalysis(Dictionary<long, decimal> workloadDistribution, int totalTasks, int totalPersonnel)\\r\\n1249:        private List<WorkOrderEntity> BuildValidationContext(\\r\\n1317:        private Task<bool> IsSecondOrThirdShift(WorkOrderEntity task)\\r\\n1369:        private int EstimatePersonnelLevel(long personnelId)\\r\\n1412:        private double CalculateQualificationScore(long personnelId)\\r\\n1456:        private double CalculateExperienceScore(long personnelId)\\r\\n1497:        private double CalculateSeniorityScore(long personnelId)\\r\\n1543:        private async Task<bool> CheckQualificationCompatibilityAsync(long taskId, long personnelId, GlobalAllocationContext context)\\r\\n1623:        private bool IsSkillMatched(decimal taskComplexity, int personnelLevel)\\r\\n1636:        private string ExtractProjectFromWorkOrderCode(string workOrderCode)\\r\\n1649:        private double CalculateSkillMatchingBonus(WorkOrderEntity task, long personnelId, GlobalAllocationContext context)\\r\\n1672:        private double CalculateAssignmentScore(long taskId, long personnelId, GlobalAllocationContext context)\\r\\n1694:        private double CalculateGiniCoefficient(List<decimal> values)\\r\\n1714:        private GlobalOptimizedSolution CreateFailureSolution(string errorMessage, long elapsedMs)\\r\\n1734:        private async Task<Dictionary<long, List<string>>> ExecuteBatchAwareValidationAsync(\\r\\n1831:        private List<WorkOrderEntity> BuildBatchSimulationContext(\\r\\n1867:        private List<string> CheckBatchTimeConflicts(\\r\\n1897:        private List<string> CheckBatchWorkloadLimits(\\r\\n1933:        private List<string> CheckPersonnelUnavailability(\\r\\n1960:        private bool HasTimeConflictInCombination(\\r\\n1988:        private bool IsTimeSlotOverlap(WorkOrderEntity task1, WorkOrderEntity task2)\\r\\n2018:        private PersonnelTaskMatrix BuildPersonnelTaskMatrix(\\r\\n2051:        private int CreateGlobalTimeSlotConstraints(\\r\\n2104:        private int CreateBatchIntegrityConstraints(\\r\\n2192:        private async Task<int> AddPersonnelUnavailabilityConstraints(\\r\\n2259:        private bool IsPersonnelUnavailableForTask(WorkOrderEntity task, long personnelId, GlobalAllocationContext context)\\r\\n2311:        private async Task<bool> IsPersonnelUnavailableForTaskAsync(WorkOrderEntity task, long personnelId, GlobalAllocationContext context)\\r\\n2324:    public class PersonnelTaskMatrix\\r\\n2326:        public Dictionary<long, List<WorkOrderEntity>> PersonnelTaskMap { get; set; } = new();\\r\\n2327:        public Dictionary<long, List<GlobalPersonnelInfo>> TaskPersonnelMap { get; set; } = new();\\r\\n2333:    public class TimeWindow : IEquatable<TimeWindow>\\r\\n2335:        public DateTime Date { get; set; }\\r\\n2336:        public long ShiftId { get; set; }\\r\\n2337:        public TimeSpan StartTime { get; set; }\\r\\n2339:        public bool Equals(TimeWindow other)\\r\\n2347:        public override bool Equals(object obj) => Equals(obj as TimeWindow);\\r\\n2349:        public override int GetHashCode() => HashCode.Combine(Date.Date, ShiftId, StartTime);\\r\\n2355:    public enum ConflictSeverity\\r\\n2367:    public static class WorkOrderEntityExtensions\\r\\n2374:        public static WorkOrderEntity CloneForValidation(this WorkOrderEntity original)\\r\\n2403:    public class SolutionQualityMetrics\\r\\n2405:        public double TaskCompletenessRate { get; set; }\\r\\n2406:        public double TimeConflictRate { get; set; }\\r\\n2407:        public double LoadBalanceScore { get; set; }\\r\\n2408:        public double PersonnelUtilizationRate { get; set; }\\r\\n2409:        public double ConstraintSatisfactionRate { get; set; }\\r\\n2410:        public List<string> ViolationDetails { get; set; } = new();\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.2}}"}}
{"timestamp":"2025-09-22T13:06:57.255Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:07:00.025Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Mapping key methods in file**"}}
{"timestamp":"2025-09-22T13:07:01.170Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":78197,"cached_input_tokens":66048,"output_tokens":1895,"reasoning_output_tokens":1216,"total_tokens":80092},"last_token_usage":{"input_tokens":15453,"cached_input_tokens":13824,"output_tokens":142,"reasoning_output_tokens":64,"total_tokens":15595},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:07:01.170Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Mapping key methods in file**"}],"content":null,"encrypted_content":"gAAAAABo0Un1ZMTp36NSBGLr_hnfTqiZvl3lZmdnRnJl3JdzoKmsHNGcEMzTgY-w-Ur_zA65wnwY76MEG4RhrlQ5KhzK9wj3Ii0qjSiFmvYpE-8dZz0dhcDu8XPM0CF8ozmuKZ6Nn3GGMgVeliS5zkMr0VfBPQVEgtpH3wBrn54gcUci1TlpMVeCz3Eg7eL7FYHSzf0gdfFfzJuLFSVkDfhwo9Fmd1tFOjulB6ESHFIaPabOWMndMR7trPZiyqzuGKRRGUU_QWc0Ew32QcMhkMspbDWIa_dovdmu4bPogVtH37hMyKjo1rX2uQMwAb_cI3yBebTX8F_bn8rbn1XVhVQBsJ0NpRtqbeXTm3uxBBZJGfa9w7VHYVgSK8zcolsSEHx8zR_ozETgTomn17NgZ9f-0IUTrYJ6ZnijjqtxoMDuSrf7bXV7ar3guTTHXfjjvAjjZTSIcZ5eNnbKp4ADi5Pa1HLwPWXxrt0rsNVpq7PmTCZUjYL-gNbN04WUcHz0bGUrEFHlGHNom_EIVqhOuT_cQjpuccPil4KAr9SKFDODCOxjCF18K2ok_4g7SluH0CQs_Dm300XJPS5p-WUy5GsMO_dAXrqmu_Uh03hpS2TKofpp3DXFQ320cvWxcDNO0anmMVzYPLezPiTEvtXc6_t5jUk2C4KUzAblaNapVXtyZCD-rBOo07w___CUFyrikmirAfqf7WF-m0r7F5MbeS9TXIPXVd1SEXwuhjgIVyR8XCKx591Mc6dtGJQVLREI3kVZVjtRzSoOCAygqIzQVTsqtRBTBR8FOFjTTy_OjGlF2_5G-wL0LfB4l-BIspGo0Do6KhpV8OoOcfcBOd8zzUS1T3pinX7FjT_OvWyaDGl5dOyBYRhCFQYSYvFI3tJzptp2c-_9A07uAGvS9wQt0Aoe9HvU80KYxW7LnObFeYxUO3pjKEuZ5WFQZpsr5uekKXwaz4mjpS1rfdUJAL5UyVbQCYtWjC86ZOJPI3E5XxjhZNM58ZJqzD18ps-TqPSndVJ6HjTwOXA0E00EV3loXZJxjJvcWCHRabckDtEgitB1nWszA1_Zd7ebIw2qw_-nwB21eKdFa7ME8JkL8ur1fLuynjy2FRDnMfvDEMokXhICs7Ti-dSqHrK9OVzBgUl3cqOHor7HDp2YOMeynIu3CkVedUDwZlDb_i3bwkEsOC_v_4E4R4jFkv5K8cHMh5_4DEgZZags36UmOZ84YJkri22RZpMU4yVEidKHX_nHUnHmQnWidygrRTgGCBVRVE_LzKcm-R2vX8-QsEj2SDZ7snoefftdwtnJ7Y0aCNNjjR8m7Y7D2dH1LbEH6UHKnQ0IAsimD1btbto2dIxCz3X6SgWEOlVc6OoiaEVJy1WRkj3tegaguygFnFpM-S1NmrlLE-OFmFYe4C4s"}}
{"timestamp":"2025-09-22T13:07:01.170Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Index 200..320\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_jPhP2ZCEmnU3jzqJaUxidaaF"}}
{"timestamp":"2025-09-22T13:07:01.170Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_jPhP2ZCEmnU3jzqJaUxidaaF","output":"{\"output\":\"Select-Object : �޷��󶨲�����Index�����޷���ֵ��200..320��ת��Ϊ���͡�System.Int32��������:�������ַ����ĸ�ʽ����ȷ����\\r\\n����λ�� ��:1 �ַ�: 122\\r\\n+ ... Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Index 200..320\\r\\n+                                                                  ~~~~~~~~\\r\\n    + CategoryInfo          : InvalidArgument: (:) [Select-Object]��ParameterBindingException\\r\\n    + FullyQualifiedErrorId : CannotConvertArgumentNoMessage,Microsoft.PowerShell.Commands.SelectObjectCommand\\r\\n \\r\\n\",\"metadata\":{\"exit_code\":1,\"duration_seconds\":0.4}}"}}
{"timestamp":"2025-09-22T13:07:01.171Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:07:03.253Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Adjusting PowerShell object selection approach**"}}
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{"timestamp":"2025-09-22T13:07:05.256Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 200 -First 140\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_hMAmhHMzuitUcWqW2GIKr57Z"}}
{"timestamp":"2025-09-22T13:07:05.256Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_hMAmhHMzuitUcWqW2GIKr57Z","output":"{\"output\":\"        /// <summary>\\r\\n        /// 构建辅助变量 - 支持复杂约束的线性化建模\\r\\n        /// 包含：负载均衡变量、时间冲突指示变量、班次规则违规变量等\\r\\n        /// </summary>\\r\\n        private Dictionary<string, Variable> BuildAuxiliaryVariables(\\r\\n            Solver solver, GlobalAllocationContext context, \\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVariables)\\r\\n        {\\r\\n            var auxiliaryVars = new Dictionary<string, Variable>();\\r\\n\\r\\n            // 1. 人员工作负载变量 (连续变量，表示每个人员的任务数量)\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                var workloadVar = solver.MakeIntVar(0, context.Tasks.Count, $\\\"workload_{personnel.Id}\\\");\\r\\n                auxiliaryVars[$\\\"workload_{personnel.Id}\\\"] = workloadVar;\\r\\n            }\\r\\n\\r\\n            // 2. 负载均衡偏差变量 (支持公平性目标建�?\\r\\n            var avgWorkload = (double)context.Tasks.Count / context.AvailablePersonnel.Count;\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                // 正偏差变�?(超出平均负载的部�?\\r\\n                var posDeviationVar = solver.MakeNumVar(0, context.Tasks.Count, $\\\"pos_dev_{personnel.Id}\\\");\\r\\n                auxiliaryVars[$\\\"pos_dev_{personnel.Id}\\\"] = posDeviationVar;\\r\\n                \\r\\n                // 负偏差变�?(低于平均负载的部�?\\r\\n                var negDeviationVar = solver.MakeNumVar(0, avgWorkload, $\\\"neg_dev_{personnel.Id}\\\");\\r\\n                auxiliaryVars[$\\\"neg_dev_{personnel.Id}\\\"] = negDeviationVar;\\r\\n            }\\r\\n\\r\\n            // 3. 时间冲突指示变量 (二进制变量，标识是否存在时间冲突)\\r\\n            var timeSlots = context.Tasks.Select(t => new { Date = t.WorkOrderDate.Date })\\r\\n                                         .Distinct().ToList();\\r\\n            \\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                foreach (var timeSlot in timeSlots)\\r\\n                {\\r\\n                    var conflictVar = solver.MakeBoolVar($\\\"conflict_{personnel.Id}_{timeSlot.Date:yyyyMMdd}\\\");\\r\\n                    auxiliaryVars[$\\\"conflict_{personnel.Id}_{timeSlot.Date:yyyyMMdd}\\\"] = conflictVar;\\r\\n                }\\r\\n            }\\r\\n\\r\\n            /*// 4. 班次规则违规变量 (支持复杂班次规则的线性化)\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                // 二班/三班后休息规则违规变�?\\r\\n                var shiftRuleViolationVar = solver.MakeIntVar(0, context.Tasks.Count, $\\\"shift_violation_{personnel.Id}\\\");\\r\\n                auxiliaryVars[$\\\"shift_violation_{personnel.Id}\\\"] = shiftRuleViolationVar;\\r\\n            }\\r\\n\\r\\n            // 5. FL优先级违规变�?(Level 3业务约束支持)\\r\\n            foreach (var task in context.Tasks)\\r\\n            {\\r\\n                var flViolationVar = solver.MakeBoolVar($\\\"fl_violation_{task.Id}\\\");\\r\\n                auxiliaryVars[$\\\"fl_violation_{task.Id}\\\"] = flViolationVar;\\r\\n            }*/\\r\\n\\r\\n            _logger.LogDebug(\\\"🔧 辅助变量构建完成 - 总数：{AuxVarCount}（工作负载：{WorkloadCount}，冲突指示：{ConflictCount}，班次违规：{ShiftCount}，FL违规：{FLCount}�?, \\r\\n                auxiliaryVars.Count,\\r\\n                context.Tasks.Count / context.AvailablePersonnel.Count,\\r\\n                context.AvailablePersonnel.Count * timeSlots.Count,\\r\\n                context.AvailablePersonnel.Count,\\r\\n                context.Tasks.Count);\\r\\n\\r\\n            return auxiliaryVars;\\r\\n        }\\r\\n\\r\\n        #endregion\\r\\n\\r\\n        #region Level 1 基础约束 - 对应遗传算法基础约束验证\\r\\n\\r\\n        /// <summary>\\r\\n        /// 添加Level 1基础约束 - 对应遗传算法的ExecuteLevel1BasicConstraintValidation\\r\\n        /// 约束内容：任务分配完整性、基本时间冲突检�?\\r\\n        /// 约束特点：必�?00%满足，违反即淘汰\\r\\n        /// </summary>\\r\\n        private async Task AddLevel1BasicConstraints(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            await Task.CompletedTask;\\r\\n            var constraintCount = 0;\\r\\n            \\r\\n            _logger.LogDebug(\\\"🏗�?开始添加Level 1基础约束...\\\");\\r\\n\\r\\n            // 基础约束1：任务分配完整�?- 每个任务必须且只能分配给一个人�?\\r\\n            foreach (var task in context.Tasks)\\r\\n            {\\r\\n                var taskVariables = decisionVars.Where(v => v.Key.TaskId == task.Id).Select(v => v.Value).ToList();\\r\\n                if (taskVariables.Any())\\r\\n                {\\r\\n                    var constraint = solver.MakeConstraint(1, 1, $\\\"task_assignment_{task.Id}\\\");\\r\\n                    foreach (var variable in taskVariables)\\r\\n                    {\\r\\n                        constraint.SetCoefficient(variable, 1);\\r\\n                    }\\r\\n                    constraintCount++;\\r\\n                }\\r\\n            }\\r\\n\\r\\n            // 基础约束2：基本时间冲突约�?- 同一人员不能在同一时间段执行多个任�?\\r\\n            // 这里使用线性约束替代遗传算法中的CalculateBasicTimeConflictScore逻辑\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                // 按时间段分组检查冲�?\\r\\n                var timeGroups = context.Tasks.GroupBy(t => new { Date = t.WorkOrderDate.Date });\\r\\n                \\r\\n                foreach (var timeGroup in timeGroups)\\r\\n                {\\r\\n                    var timeSlotTasks = timeGroup.ToList();\\r\\n                    if (timeSlotTasks.Count > 1) // 同一时间段有多个任务才需要约�?\\r\\n                    {\\r\\n                        var timeSlotVariables = timeSlotTasks\\r\\n                            .Where(task => decisionVars.ContainsKey((task.Id, personnel.Id)))\\r\\n                            .Select(task => decisionVars[(task.Id, personnel.Id)])\\r\\n                            .ToList();\\r\\n\\r\\n                        if (timeSlotVariables.Count > 1)\\r\\n                        {\\r\\n                            // 约束：同一人员在同一时间段最多执�?个任�?\\r\\n                            var constraint = solver.MakeConstraint(0, 1, \\r\\n                                $\\\"time_conflict_{personnel.Id}_{timeGroup.Key.Date:yyyyMMdd}\\\");\\r\\n                            \\r\\n                            foreach (var variable in timeSlotVariables)\\r\\n                            {\\r\\n                                constraint.SetCoefficient(variable, 1);\\r\\n                            }\\r\\n                            constraintCount++;\\r\\n\\r\\n                            // 连接辅助变量（用于目标函数惩罚）\\r\\n                            var conflictVarKey = $\\\"conflict_{personnel.Id}_{timeGroup.Key.Date:yyyyMMdd}\\\";\\r\\n                            if (auxiliaryVars.ContainsKey(conflictVarKey))\\r\\n                            {\\r\\n                                // 如果分配了多个任务，冲突变量=1\\r\\n                                var conflictConstraint = solver.MakeConstraint(0, double.PositiveInfinity, \\r\\n                                    $\\\"conflict_indicator_{personnel.Id}_{timeGroup.Key.Date:yyyyMMdd}\\\");\\r\\n                                \\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
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{"timestamp":"2025-09-22T13:07:07.966Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Extending snippet review**"}}
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{"timestamp":"2025-09-22T13:07:08.875Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_G5sJeoHADpi56VSR0ObwDYEf","output":"{\"output\":\"            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            await Task.CompletedTask;\\n            var constraintCount = 0;\\n            \\n            _logger.LogDebug(\\\"🏗�?开始添加Level 1基础约束...\\\");\\n\\n            // 基础约束1：任务分配完整�?- 每个任务必须且只能分配给一个人�?\\n            foreach (var task in context.Tasks)\\n            {\\n                var taskVariables = decisionVars.Where(v => v.Key.TaskId == task.Id).Select(v => v.Value).ToList();\\n                if (taskVariables.Any())\\n                {\\n                    var constraint = solver.MakeConstraint(1, 1, $\\\"task_assignment_{task.Id}\\\");\\n                    foreach (var variable in taskVariables)\\n                    {\\n                        constraint.SetCoefficient(variable, 1);\\n                    }\\n                    constraintCount++;\\n                }\\n            }\\n\\n            // 基础约束2：基本时间冲突约�?- 同一人员不能在同一时间段执行多个任�?\\n            // 这里使用线性约束替代遗传算法中的CalculateBasicTimeConflictScore逻辑\\n            foreach (var personnel in context.AvailablePersonnel)\\n            {\\n                // 按时间段分组检查冲�?\\n                var timeGroups = context.Tasks.GroupBy(t => new { Date = t.WorkOrderDate.Date });\\n                \\n                foreach (var timeGroup in timeGroups)\\n                {\\n                    var timeSlotTasks = timeGroup.ToList();\\n                    if (timeSlotTasks.Count > 1) // 同一时间段有多个任务才需要约�?\\n                    {\\n                        var timeSlotVariables = timeSlotTasks\\n                            .Where(task => decisionVars.ContainsKey((task.Id, personnel.Id)))\\n                            .Select(task => decisionVars[(task.Id, personnel.Id)])\\n                            .ToList();\\n\\n                        if (timeSlotVariables.Count > 1)\\n                        {\\n                            // 约束：同一人员在同一时间段最多执�?个任�?\\n                            var constraint = solver.MakeConstraint(0, 1, \\n                                $\\\"time_conflict_{personnel.Id}_{timeGroup.Key.Date:yyyyMMdd}\\\");\\n                            \\n                            foreach (var variable in timeSlotVariables)\\n                            {\\n                                constraint.SetCoefficient(variable, 1);\\n                            }\\n                            constraintCount++;\\n\\n                            // 连接辅助变量（用于目标函数惩罚）\\n                            var conflictVarKey = $\\\"conflict_{personnel.Id}_{timeGroup.Key.Date:yyyyMMdd}\\\";\\n                            if (auxiliaryVars.ContainsKey(conflictVarKey))\\n                            {\\n                                // 如果分配了多个任务，冲突变量=1\\n                                var conflictConstraint = solver.MakeConstraint(0, double.PositiveInfinity, \\n                                    $\\\"conflict_indicator_{personnel.Id}_{timeGroup.Key.Date:yyyyMMdd}\\\");\\n                                \\n                                foreach (var variable in timeSlotVariables)\\n                                {\\n                                    conflictConstraint.SetCoefficient(variable, 1);\\n                                }\\n                                conflictConstraint.SetCoefficient(auxiliaryVars[conflictVarKey], -timeSlotVariables.Count + 1);\\n                                constraintCount++;\\n                            }\\n                        }\\n                    }\\n                }\\n            }\\n\\n            // 基础约束3：工作负载计算约�?- 连接决策变量与辅助变�?\\n            foreach (var personnel in context.AvailablePersonnel)\\n            {\\n                var personnelTasks = decisionVars.Where(v => v.Key.PersonnelId == personnel.Id).ToList();\\n                if (personnelTasks.Any() && auxiliaryVars.ContainsKey($\\\"workload_{personnel.Id}\\\"))\\n                {\\n                    var constraint = solver.MakeConstraint(0, 0, $\\\"workload_calc_{personnel.Id}\\\");\\n                    \\n                    foreach (var kvp in personnelTasks)\\n                    {\\n                        constraint.SetCoefficient(kvp.Value, 1);\\n                    }\\n                    constraint.SetCoefficient(auxiliaryVars[$\\\"workload_{personnel.Id}\\\"], -1);\\n                    constraintCount++;\\n                }\\n            }\\n\\n            _logger.LogDebug(\\\"�?Level 1基础约束添加完成 - 约束数量：{ConstraintCount}\\\", constraintCount);\\n        }\\n\\n        #endregion\\n\\n        #region Level 2 组合约束 - 对应遗传算法组合约束验证\\n\\n        /// <summary>\\n        /// 添加Level 2组合约束 - 对应遗传算法的ExecuteLevel2CombinationConstraintValidation\\n        /// 约束内容：动态班次规则、跨任务资源竞争约束\\n        /// 约束特点：基于任务组合的动态验证，违反时重度惩�?\\n        /// </summary>\\n        private asyn\\n[... omitted 0 of 200 lines ...]\\n\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            var constraintCount = 0;\\n            _logger.LogInformation(\\\"🚀 启动增强版批次感知班次规则约束构�?..\\\");\\n\\n            // 【架构创�?】构建人员任务关联矩阵，支持高效的批次验�?\\n            var personnelTaskMatrix = BuildPersonnelTaskMatrix(decisionVars, context);\\n            _logger.LogDebug(\\\"📊 人员任务矩阵构建完成 - 人员映射：{PersonnelCount}，任务映射：{TaskCount}\\\", \\n                personnelTaskMatrix.PersonnelTaskMap.Count, personnelTaskMatrix.TaskPersonnelMap.Count);\\n\\n            // 【架构创�?】为每个人员构建批次感知的验证上下文\\n            var batchValidationResults = new Dictionary<long, Dictionary<long, List<string>>>();\\n            \\n            foreach (var personnelEntry in personnelTaskMatrix.PersonnelTaskMap)\\n            {\\n                var personnelId = personnelEntry.Key;\\n                var possibleTasks = personnelEntry.Value;\\n                \\n                if (possibleTasks.Count > 1) // 只对有多个可能任务的人员进行批次验证\\n                {\\n                    batchValidationResults[personnelId] = await ExecuteBatchAwareValidationAsync(\\n                        personnelId, possibleTasks, context, personnelTaskMatrix);\\n                }\\n            }\\n\\n            // 【架构创�?】基于批次验证结果创建约�?\\n            var shiftViolationVars = new Dictionary<string, Variable>();\\n            \\n            foreach (var kvp in decisionVars)\\n            {\\n                var (taskId, personnelId) = kvp.Key;\\n                var assignmentVar = kvp.Value;\\n                \\n                // 创建该分配的班次规则违规指示变量\\n                var violationVarKey = $\\\"batch_aware_shift_violation_{taskId}_{personnelId}\\\";\\n                var violationVar = solver.MakeBoolVar(violationVarKey);\\n                shiftViolationVars[violationVarKey] = violationVar;\\n                \\n                // 【批次感知验证】检查该分配在批次上下文中的合规�?\\n                var hasViolation = false;\\n                \\n                if (batchValidationResults.TryGetValue(personnelId, out var personnelValidationResults))\\n                {\\n                    if (personnelValidationResults.TryGetValue(taskId, out var violations))\\n                    {\\n                        hasViolation = violations.Any();\\n                        \\n                        if (hasViolation)\\n                        {\\n                            _logger.LogDebug(\\\"📋 批次感知验证发现冲突 - 任务{TaskId}人员{PersonnelId}：{Violations}\\\", \\n                                taskId, personnelId, string.Join(\\\"; \\\", violations.Take(2)));\\n                        }\\n                    }\\n                }\\n                else\\n                {\\n                    // 【后备验证】单任务人员使用传统验证\\n                    var task = context.Tasks.FirstOrDefault(t => t.Id == taskId);\\n                    if (task != null)\\n                    {\\n                        var otherAssignments = BuildValidationContext(personnelId, taskId, context, decisionVars);\\n                        var conflicts = await _shiftRuleEngine.ValidateShiftRulesWithEnhancedBatchContextAsync(\\n                            personnelId, task, otherAssignments, context);\\n                        hasViolation = conflicts.Any();\\n                    }\\n                }\\n\\n                // 【约束建模】基于验证结果创建约�?\\n                if (hasViolation)\\n                {\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
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{"timestamp":"2025-09-22T13:07:11.259Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Updating plan after mapping layout**"}}
{"timestamp":"2025-09-22T13:07:11.794Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":131415,"cached_input_tokens":114944,"output_tokens":2126,"reasoning_output_tokens":1216,"total_tokens":133541},"last_token_usage":{"input_tokens":19708,"cached_input_tokens":17664,"output_tokens":71,"reasoning_output_tokens":0,"total_tokens":19779},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:07:11.795Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Updating plan after mapping layout**"}],"content":null,"encrypted_content":"gAAAAABo0UoA-vSvV9jRsUH50L8ilmqO6lYWvNpK6ppyPktYuPOfNX4keu3Uhuy0p39S7-_EHzqibj1vDtAVoQ7uFEgU36gmbDfOf6cqsTZutJHDlZ90KF--Pt5p_uq3qOK1uxu88_07540aNjrwuvYXIUWppmd5UI23D84QMCo_zHUoiAlIbQsr008zHIMi0nsae5WnKZGRS8rbJA98ygEc5-eO0YF9FurXrluCJfuWHkc3EAPJX_wDtNZPzGkOeOhYMDq4zZJXkmOxeISRHTfQa_NGGonWM57EEs1J3ACObh-JcgPiTOz1tyVu4TQRNNd0gdRLMLCHZIoLUy1OpbQLuJ58cIgezuOU-MGLSaBzXidmBJjwgMRRgIyk52uliFOSXcIfaxLzXBDJtBHSt5fh9CtIId-3d2abf8HsdI3Rr5W9M2i9vvy8c9wAkKPS7YmRox66KzLQ8lbJ7Kn4ewk2vWaAZ2hivE74PMOWQcn9ddxANuuSOgUQwUtsfgi0t8Eba8ke9mJtn-eG5y9FzqBfAUJxzgMnLn5nwsSLbsdZRGYegSrCF3AKTp067focvPCAdysOjqADp3b2uzZL64M3SSWNwW4rXlKnnp1L_e6kxAjh4k1kh0ZqXr4UsamsI6c9-ESeXv20W7JR3x0XswbiZx0cerI16Q-qXPPfgMJXYGYll45CAsrJKUILJ7898thnohyRDTr-7EgwvGwPMw09WeLmkfUiQfyJaaXcLgeZhEAVCJmwPDhYqWe4Jx4lIMNATbi5DiLsQ-ShfZqW1IAzI6-v08fXNzIMD7LQM3zo4fARUgZRNQO2D7yvdTWev_mFs6zcVdMfTdMThMgvfcpUZ5Kwv4DFLbeqWchha2PZRTGG1UNZ-HZBlYDLyf4VsTawlGGrTaH7ZRWmfiu3GAGBX6cySfJT_y6YNgcVMlksKQpDocVNtkQPZOV5MlYAhGjNxbV8VBgBhymz-U-sPJuwoDh20vEWJOgfHwQE2GJCHS6hIJnsFaxcqePrf4WZ5yVBSinatq1t3KsQdng8J4Y_7_Hg83rncXCC-LtgPCNpDIQ78-fz4pveJxfvnhKakpVGENRx4tP5Ec1FiWcfGVdkznZlZy3LawMScGzZTbP3xuUC0nnUeobQUIlvYE5AQj4hCiCPKhCUbCAcdg-NswHbo4ELeSLEvARtehrhxiPrZnhD7RuIFzum60F-VZcYKU4pX-f41mvk"}}
{"timestamp":"2025-09-22T13:07:11.795Z","type":"response_item","payload":{"type":"function_call","name":"update_plan","arguments":"{\"plan\":[{\"status\":\"completed\",\"step\":\"Skim class layout to map major responsibilities\"},{\"status\":\"in_progress\",\"step\":\"Review critical methods for modeling, constraint setup, and objective\"},{\"status\":\"pending\",\"step\":\"Summarize findings, risks, and improvement ideas\"}]}","call_id":"call_m5ufiFyuu9b2mcf5QyJxvRJ5"}}
{"timestamp":"2025-09-22T13:07:11.795Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_m5ufiFyuu9b2mcf5QyJxvRJ5","output":"Plan updated"}}
{"timestamp":"2025-09-22T13:07:11.795Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:07:16.522Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Analyzing key methods for issues**"}}
{"timestamp":"2025-09-22T13:07:17.357Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":151263,"cached_input_tokens":134656,"output_tokens":2334,"reasoning_output_tokens":1344,"total_tokens":153597},"last_token_usage":{"input_tokens":19848,"cached_input_tokens":19712,"output_tokens":208,"reasoning_output_tokens":128,"total_tokens":20056},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:07:17.357Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Analyzing key methods for issues**"}],"content":null,"encrypted_content":"gAAAAABo0UoF_sr3Cu3q_1itKwlKm8CRIrcTD05LtHPzTePRoJR03t6JG2ZbILT1Ygrky25wMXvZid1HFrNX52OhfDRle9N-sdg6PkvJba1w6jPPbMWLbkB3Px4kbRnI_2eyEv8h2MGAg0uBuN9YU1c5hvEGephX5QgcRAezqd5P23oxUXW3kw8ewcUvHLPQlhoa7vEmRXTMyK-c9wWYjMnyg3aJzRMwwVngRXmyi5ORjHk6-LWeUYnbjNChkukzmPPygAeesAUOAWJKOXl7LJrLcBcMTs4wikDgTaxxWm8-1iGBSnXBsQipim6fdN-47gPprl7COsSW1WV8mW-RSAOI1FbgncSP3WF4MCy2LM_tZIi0Wo4obYBV-wfbyzOSrPZu0bJ8-qJ3UgfzSS3oBBIUzkbm2y482IW97IXUb_5ABfg7MxCWUk1aehOeKpfCRRLqZvBUj8yE8ccDG5o05zbfAwQ46HGlUNrCX1G5gI_e37v58a9big6_sVvv8uKg8tb2wdZ2N8djrp68QlQmWdh-jpcnQ8VHNTJseoywb_6RPNamkOgay7jTauMB1NVKVKHmfG07QM9QSv-DKkNsR6C0Q7xKsun_8H61ZG1pzF5ks6sB9rBea17B1aQCuX0B0iWNDtGFf0MrjISssqy0edY9PrEIZck6_nY7CuR8_zxhTQ_Rg5b86pncsIkncWbnJRXNtx3wiF9A-AbXAHyKaIvj6jtC4HEM9TqDmlrs6aavmd8gvnsSK_GLLn9IZMRC3oeTgdx3ZQ3DkavSp49_-5pE1Ag9e2UZ0ARazyJXR7YOAIFvdWo9167EyTSxRVJwR1qqmc6-ipCrDnx3-MzbpmlzmQWqStxdEc70tlsMMQJeIp5KJEQ3cJcyAfT_vJ45sT8kE3701sLKFO-bZbJzTjT0KJAWGiKtr5_IrV1xi5uDWCNFnru7_jME6U8PwwKLnkFUK49TdkeveXHT9PN4t9bVpySA_2Nlp0-emN-4-9vSMh4LriK5X2KMyCNVOLysKTflTCOEZOkhPmCoGRJPL1Iatp-sTesIM6DDVyaI47Ffy6M9LRuxrpW8ETbie9IRS55y3-aUBiWaSCzwvT5ycDvJ7dgyNQ4fiILgIJBwuDL6StSzrRNqJ3z6HvQNS2vIxnx2ps8W_-0mp_E-f787UDc5xfmDhCrOEpAYW5RufoeY48VgZkz5_iUoNNtqN8GSjTrJbWHENl5ji_st-594AUZswGaPh5xUwyaMeckg-E4EEIYginCVkpsttgFCZytSR7iPH27VdVguNemMm6uQ5gE-_CdxCRBuAhgBuCIjlqQTydc0iV4GU_eopeowCIlHkxEmFExj1j2c2xL_aj8RTOo3KDPgg77SIANcL3XwcTBcEyPLEuVdNQlRf1DiurTKM_LK9zLHFpPdukAMsdh6jO1eguX6rKDfJJFcdA-JbGmqrvgDLzLwglpFFDSlXP6Tzo-xJLYmEftQku5uDkCW9cUdKJxEgMrYE-HvrIV2IHHiajK-W3VYpMWdLncsdbedCko7h7OIkpS4xd_s5fwuSiT8I8IrLmXHwWZuXDK7yHN2HS6i5q4rO25-MAtxOnngC_pjlM3K8ISV7n_mXUz0NZ_qLYW_hOFBxG9nrZXAneJRS6NVYgJniCm9P7oF6YwSRooSSTuym6uLwrR8Klu9V_PyIeHuWFkP9m-X26INYYPMU9aR47P-X1RGATo2YNKQ2FStwZgom9WXG8eCjPRKhSqMOTTxfT4nyW_XXnX3NcSkFA8VNDkSX-jKwc9WyjkG07kvqKbpAV3uWkJrBeLDrLwN4EWTdgQInIDjSB_ZI7e02KVFReH3y3_R9b2VT-J_Wb8PPHcVIAyj"}}
{"timestamp":"2025-09-22T13:07:17.357Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 40 -First 130\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_X7bp7jSUXKP7niQRrjFsjdgp"}}
{"timestamp":"2025-09-22T13:07:17.357Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_X7bp7jSUXKP7niQRrjFsjdgp","output":"{\"output\":\"        {\\r\\n            _logger = logger ?? throw new ArgumentNullException(nameof(logger));\\r\\n            _shiftRuleEngine = shiftRuleEngine ?? throw new ArgumentNullException(nameof(shiftRuleEngine));\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 执行增强版线性规划优�?- 实现遗传算法等价的约束体�?\\r\\n        /// 业务流程：构建决策变�?�?三级约束建模 �?多目标优�?�?精确求解 �?解析结果\\r\\n        /// 核心创新：将启发式约束转换为线性约束，实现数学最优解\\r\\n        /// </summary>\\r\\n        public async Task<GlobalOptimizedSolution> OptimizeAsync(GlobalAllocationContext context)\\r\\n        {\\r\\n            var stopwatch = System.Diagnostics.Stopwatch.StartNew();\\r\\n            \\r\\n            try\\r\\n            {\\r\\n                _logger.LogDebug(\\\"🚀 启动增强版线性规划优�?- 任务数：{TaskCount}，人员数：{PersonnelCount}，预筛选缓存：{CacheSize}\\\", \\r\\n                    context.Tasks.Count, context.AvailablePersonnel.Count, context.PrefilterResults.Count);\\r\\n\\r\\n                // 第一步：创建高性能求解�?\\r\\n                using var solver = CreateAdvancedSolver();\\r\\n\\r\\n                // 第二步：构建决策变量矩阵 (x[i,j] = 1表示任务i分配给人员j)\\r\\n                var decisionVariables = BuildDecisionVariables(solver, context);\\r\\n                _logger.LogDebug(\\\"📊 决策变量构建完成 - 变量数量：{VariableCount}\\\", decisionVariables.Count);\\r\\n\\r\\n                // 第三步：构建辅助变量 (支持复杂约束建模)\\r\\n                var auxiliaryVariables = BuildAuxiliaryVariables(solver, context, decisionVariables);\\r\\n                _logger.LogDebug(\\\"🔧 辅助变量构建完成 - 辅助变量数量：{AuxCount}\\\", auxiliaryVariables.Count);\\r\\n\\r\\n                // 第四步：Level 1 基础约束 - 对应遗传算法的基础约束验证\\r\\n                await AddLevel1BasicConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第五步：Level 2 组合约束 - 对应遗传算法的组合约束验�? \\r\\n                await AddLevel2CombinationConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第六步：Level 3 业务约束 - 对应遗传算法的业务逻辑约束\\r\\n                await AddLevel3BusinessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第七步：公平性约�?- 对应遗传算法的负载均衡机�?\\r\\n                AddFairnessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第八步：设置多维度目标函�?- 对应遗传算法的适应度函�?\\r\\n                SetEnhancedObjectiveFunction(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第九步：执行求解\\r\\n                _logger.LogInformation(\\\"🔍 开始求解增强版线性规划模�?..\\\");\\r\\n                var resultStatus = solver.Solve();\\r\\n                \\r\\n                stopwatch.Stop();\\r\\n                \\r\\n                // 第十步：解析和验证结�?\\r\\n                var solution = await ProcessEnhancedSolverResult(solver, decisionVariables, auxiliaryVariables, \\r\\n                    context, resultStatus, stopwatch.ElapsedMilliseconds);\\r\\n                \\r\\n                return solution;\\r\\n            }\\r\\n            catch (Exception ex)\\r\\n            {\\r\\n                stopwatch.Stop();\\r\\n                _logger.LogError(ex, \\\"💥 增强版线性规划优化异常，耗时：{ElapsedMs}ms\\\", stopwatch.ElapsedMilliseconds);\\r\\n                \\r\\n                return CreateFailureSolution(ex.Message, stopwatch.ElapsedMilliseconds);\\r\\n            }\\r\\n        }\\r\\n\\r\\n        #region 求解器和变量构建\\r\\n\\r\\n        /// <summary>\\r\\n        /// 创建高性能求解�?- 支持大规模约束优�?\\r\\n        /// </summary>\\r\\n        private Solver CreateAdvancedSolver()\\r\\n        {\\r\\n            var solver = Solver.CreateSolver(SOLVER_TYPE);\\r\\n            if (solver == null)\\r\\n            {\\r\\n                throw new InvalidOperationException(\\\"无法创建增强版线性规划求解器\\\");\\r\\n            }\\r\\n\\r\\n            // 高性能配置\\r\\n            solver.SetTimeLimit(MAX_SOLVER_TIME_SECONDS * 1000);\\r\\n            \\r\\n            // 设置优化参数\\r\\n            solver.SetSolverSpecificParametersAsString(\\r\\n                \\\"cuts=1 \\\" +           // 启用割平�?\\r\\n                \\\"heuristics=1 \\\" +     // 启用启发式算�?\\r\\n                \\\"presolve=1 \\\" +       // 启用预处�?\\r\\n                \\\"threads=0\\\"           // 使用所有可用线�?\\r\\n            );\\r\\n\\r\\n            _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n            return solver;\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 【Ultra think增强】构建决策变�?- 集成人员班次不可用性的智能变量生成\\r\\n        /// 【核心优化】：在变量创建阶段就排除不可用组合，大幅提升求解效率\\r\\n        /// 【三层优化】：预筛选结�?+ 不可用性检�?+ 评分阈值，实现精确的变量裁�?\\r\\n        /// 【性能提升】：预计减少20-40%的决策变量数量，显著提升求解速度\\r\\n        /// </summary>\\r\\n        private Dictionary<(long TaskId, long PersonnelId), Variable> BuildDecisionVariables(\\r\\n            Solver solver, GlobalAllocationContext context)\\r\\n        {\\r\\n            var variables = new Dictionary<(long TaskId, long PersonnelId), Variable>();\\r\\n            var createdCount = 0;\\r\\n            var skippedCount = 0;\\r\\n            var unavailabilitySkippedCount = 0;\\r\\n            \\r\\n            foreach (var task in context.Tasks)\\r\\n            {\\r\\n                foreach (var personnel in context.AvailablePersonnel)\\r\\n                {\\r\\n                    var prefilterKey = $\\\"{task.Id}_{personnel.Id}\\\";\\r\\n                    \\r\\n                    // 【Ultra think优化1】首先检查人员班次不可用�?- 硬排�?\\r\\n                    if (IsPersonnelUnavailableForTask(task, personnel.Id, context))\\r\\n                    {\\r\\n                        unavailabilitySkippedCount++;\\r\\n                        skippedCount++;\\r\\n                        _logger.LogDebug(\\\"🚫 跳过不可用组�?- 任务{TaskId}人员{PersonnelId}在{Date}班次{ShiftId}不可�?, \\r\\n                            task.Id, personnel.Id, task.WorkOrderDate.ToString(\\\"yyyy-MM-dd\\\"), task.ShiftId);\\r\\n                        continue;\\r\\n                    }\\r\\n                    \\r\\n                    // 【Ultra think优化2】基于预筛选结果的智能决策\\r\\n                    if (context.PrefilterResults.TryGetValue(prefilterKey, out var prefilterResult))\\r\\n                    {\\r\\n                        // 对于评分较高的分配，即使标记为不可行也创建变量（可能是保守评估）\\r\\n                        if (prefilterResult.IsFeasible || prefilterResult.PrefilterScore > 30.0)\\r\\n                        {\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:07:17.357Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:07:21.613Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Investigating AddLevel2 method details**"}}
{"timestamp":"2025-09-22T13:07:22.408Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":173107,"cached_input_tokens":154624,"output_tokens":2542,"reasoning_output_tokens":1472,"total_tokens":175649},"last_token_usage":{"input_tokens":21844,"cached_input_tokens":19968,"output_tokens":208,"reasoning_output_tokens":128,"total_tokens":22052},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:07:22.408Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Investigating AddLevel2 method details**"}],"content":null,"encrypted_content":"gAAAAABo0UoK5vLVcUFziFbsWjzYO9KTpo2kg1kXVKVPHVMkIZQSzAwb9RGwzxJHkoZljb56fgQFHFya9dQc3Lc6nd9g9Go-y2Ofvj5Gu4N7hSmUW2ITW1gU3oPTCiMo4H2AEbL9TnS2MtCxpyJGn88dZBvzjx9NIq2qh3mJX6YO07ZW9e_hpHi8vrMwqFtSqsQeQzQk1Dnn5fgCt8XScRAyKpcHzdUlXygxJDlHAiZ_726TZAhEdStBaBVFaKVVxJk1LiqjjyDTbPFexdvN6fk3lH7BsTAw08DebHXoDzn0oBC--BTdMweSHWSv2lnLtYrklROAERPi2TXX7HtepVL7MOHMEgOxh4QJEhFHbiRFE4QwhgkP5PT8Btli7B7ZCEvCx9GZ_AW4WBDvPNFgwCS6C5TEZ0e71TAlF4bIUPw0ULfO4DMLuQ_s5GBmMYJpO5OpI30F0vIbyVryTCzNRuux76tdlI5ynmRAJHFQ55ltpYfBWaDxyhvfB2QGW9CKqQlYu-rN4ltXt3YoqkTgEp6e1tZ4jBzo1sHUV9yHnuqQyLmn-A4sCCjDYxhQ62kAHXgH4r6_qJ1LVkv1U6apYt1Zr5pDqHRIqfCTU-p7rDrCd8Q0gjLs45x3rXcWpGE2ogyRrm7sJ_5agHUUseo_vLh_Qk5urrEZmDEpJnQ75unYtrnG3E7v03uWdAx3ERhZYWHAiaeuqaz7TMwlu7dbHGdhKoanGa4UeqooiJb7D8tr0y6H3mFoDBWPtbx6T_4vIG092MLK3CIa-WYddqD450crGEfeJ9Ah4pU1XXSKw1KcXIIaSYA4RxjlfsSMXpSEWnLBlPpTrOuYoWOtyC7Uk6b13CR8X9HfkzM4fkvD18OCAXxFwibGl2iPa0QRZkFkhy-6eIIq7YcwA6gqfwKgXSdKJpJbxVNixygbmViowgV47_AhCBbstrxuiflqIjvsCSNFuO0mNb5rBJ8XWPEgAIoYM6h0jD1sS5XvVaxQucrqJrB1jEFYRp-v9RrYjuH0ZtZnGLtfjTbYnXD6b85YeuvsWxfDe8_oDjieFi_twmgxJgLMHq1vizN6SGANwmZVkxtneDJWPPy9Lil7iHE4ApcSYsmNZxBCb1fBF6OhKZPSGekPv77n2dGlBSi9Ejp0hNMUtFLtEL8_J7_MhTZRUhVLf15Z9F4fvrhFkYdRnwNRx6RcqiSI72cEIeU3ECyV1NYneOWS4daKEqrKMz2oeSQq1w1MH5m9Xk-mP-k0f15pUiY3349JOhL7KkwdUnFZ9lWH4F60zSOHZyNXiYX9HOFKO6bpgStzXHLrY8bagaAUfemZ9AoS84zc7KalbADl7VD9JKWiGzEH2O8yXraYyb_x94TL5MybsvtkKCQGrVur5pRRwz68JtiRRfW7vLygnek8_wKmyGcPR5w7JN3DkWKm6Xf1oOPdN-C9GrNw-XLN7Ya5kmu8Taf3OeQm7V2JaM4XaVux1_1y11no4Q0uW1TGtX-L41LoZShB04lGu4o8OW8IHsFRZR4dXYv63Z9VMn7GbONa52OUkuYo48n3AzwDUf92U-4NiKox77b9Aa7TLo26uTeEWNlVFXCYfqlfzCjke5Ajrp2uujnhqXGfX9_pJ_J2VvDlLddwh2o8yDyejX68xN_78MS9aW6iGOYG-k-rp4SAh8SYV-ZCcJhfmrXYe4iOV8ifv8AG1VvNPd2qWRAg6EpuTvc="}}
{"timestamp":"2025-09-22T13:07:22.408Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 360 -First 220\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_Iq3GtfrNfNRvIFN8vPnxSGH3"}}
{"timestamp":"2025-09-22T13:07:22.408Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Iq3GtfrNfNRvIFN8vPnxSGH3","output":"{\"output\":\"                    foreach (var kvp in personnelTasks)\\n                    {\\n                        constraint.SetCoefficient(kvp.Value, 1);\\n                    }\\n                    constraint.SetCoefficient(auxiliaryVars[$\\\"workload_{personnel.Id}\\\"], -1);\\n                    constraintCount++;\\n                }\\n            }\\n\\n            _logger.LogDebug(\\\"�?Level 1基础约束添加完成 - 约束数量：{ConstraintCount}\\\", constraintCount);\\n        }\\n\\n        #endregion\\n\\n        #region Level 2 组合约束 - 对应遗传算法组合约束验证\\n\\n        /// <summary>\\n        /// 添加Level 2组合约束 - 对应遗传算法的ExecuteLevel2CombinationConstraintValidation\\n        /// 约束内容：动态班次规则、跨任务资源竞争约束\\n        /// 约束特点：基于任务组合的动态验证，违反时重度惩�?\\n        /// </summary>\\n        private async Task AddLevel2CombinationConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            var constraintCount = 0;\\n            \\n            _logger.LogDebug(\\\"🔄 开始添加Level 2组合约束...\\\");\\n\\n            // 组合约束1：动态班次规则约�?- 对应CalculateDynamicShiftRuleScore\\n            //constraintCount += await AddDynamicShiftRuleConstraints(solver, decisionVars, auxiliaryVars, context);\\n            \\n            // 组合约束3：人员日工作量限制约�?\\n            //constraintCount += AddDailyWorkloadConstraints(solver, decisionVars, auxiliaryVars, context);\\n\\n            _logger.LogDebug(\\\"�?Level 2组合约束添加完成 - 约束数量：{ConstraintCount}\\\", constraintCount);\\n        }\\n\\n        /// <summary>\\n        /// 【架构重构】添加增强版批次感知班次规则约束 - 真正解决批次验证问题\\n        /// 【核心创新】：集成批次感知验证，考虑当前分配组合与历史数据的整体验证\\n        /// 【深度实现】：使用PersonnelTaskMatrix进行高效的关联查询和冲突检�?\\n        /// 【Ultra think级别】：从根本上解决单任�?历史验证的架构缺�?\\n        /// </summary>\\n        private async Task<int> AddDynamicShiftRuleConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            var constraintCount = 0;\\n            _logger.LogInformation(\\\"🚀 启动增强版批次感知班次规则约束构�?..\\\");\\n\\n            // 【架构创�?】构建人员任务关联矩阵，支持高效的批次验�?\\n            var personnelTaskMatrix = BuildPersonnelTaskMatrix(decisionVars, context);\\n            _logger.LogDebug(\\\"📊 人员任务矩阵构建完成 - 人员映射：{PersonnelCount}，任务映射：{TaskCount}\\\", \\n                personnelTaskMatrix.PersonnelTaskMap.Count, personnelTaskMatrix.TaskPersonnelMap.Count);\\n\\n            // 【架构创�?】为每个人员构建批次感知的验证上下文\\n            var batchValidationResults = new Dictionary<long, Dictionary<long, List<string>>>();\\n            \\n            foreach (var personnelEntry in personnelTaskMatrix.PersonnelTaskMap)\\n            {\\n                var personnelId = personnelEntry.Key;\\n                var possibleTasks = personnelEntry.Value;\\n                \\n                if (possibleTasks.Count > 1) // 只对有多个可能任务的人员进行批次验证\\n                {\\n                    batchValidationResults[personnelId] = await ExecuteBatchAwareValidationAsync(\\n                        personnelId, possibleTasks, context, personnelTaskMatrix);\\n                }\\n            }\\n\\n            // 【架构创�?】基于批次验证结果创建约�?\\n            var shiftViolationVars = new Dictionary<string, Variable>();\\n            \\n            foreach (var kvp in decisionVars)\\n            {\\n                var (taskId, personnelId) = kvp.Key;\\n                var assignmentVar = kvp.Value;\\n                \\n                // 创建该分配的班次规则违规指示变量\\n                var violationVarKey = $\\\"batch_aware_shift_violation_{taskId}_{personnelId}\\\";\\n                var violationVar = solver.MakeBoolVar(violationVarKey);\\n                shiftViolationVars[violationVarKey] = violationVar;\\n                \\n                // 【批次感知验证】检查该分配在批次上下文中的合规�?\\n                var hasViolation = false;\\n                \\n                if (batchValidationResults.TryGetValue(personnelId, out var personnelValidationResults))\\n                {\\n                    if (personnelValidationResults.TryGetValue(taskId, out var violations))\\n                    {\\n                        hasViolation = violations.Any();\\n                        \\n                        if (hasViolation)\\n                        {\\n                            _logger.LogDebug(\\\"📋 批次感知\\n[... omitted 0 of 220 lines ...]\\n\\n                else\\n                {\\n                    // 无冲突时强制违规变量�?\\n                    var noViolationConstraint = solver.MakeConstraint(0, 0,\\n                        $\\\"batch_no_violation_{taskId}_{personnelId}\\\");\\n                    noViolationConstraint.SetCoefficient(violationVar, 1);\\n                    constraintCount++;\\n                }\\n            }\\n\\n            // 【架构创�?】添加全局批次完整性约�?\\n            constraintCount += CreateGlobalTimeSlotConstraints(solver, decisionVars, context);\\n            constraintCount += CreateBatchIntegrityConstraints(solver, decisionVars, auxiliaryVars, context);\\n            \\n            // 【全局约束】总违规数量限�?\\n            var totalViolations = shiftViolationVars.Values.ToList();\\n            if (totalViolations.Any())\\n            {\\n                var maxAllowedViolations = Math.Max(1, context.Tasks.Count / 10);\\n                var totalViolationConstraint = solver.MakeConstraint(0, maxAllowedViolations, \\\"max_batch_shift_violations\\\");\\n                \\n                foreach (var violationVar in totalViolations)\\n                {\\n                    totalViolationConstraint.SetCoefficient(violationVar, 1);\\n                }\\n                constraintCount++;\\n                \\n                // 将违规变量添加到辅助变量集合�?\\n                foreach (var kvp in shiftViolationVars)\\n                {\\n                    auxiliaryVars[kvp.Key] = kvp.Value;\\n                }\\n            }\\n\\n            _logger.LogInformation(\\\"�?增强版批次感知班次规则约束构建完�?- 约束数量：{ConstraintCount}，批次验证人员：{BatchPersonnelCount}，违规变量：{ViolationVarCount}\\\", \\n                constraintCount, batchValidationResults.Count, shiftViolationVars.Count);\\n            return constraintCount;\\n        }\\n        \\n        /// <summary>\\n        /// 添加日工作量约束 - 限制人员每日工作负载\\n        /// </summary>\\n        private int AddDailyWorkloadConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            var constraintCount = 0;\\n            const int maxDailyHours = 8; // 每日最大工作时长（包含加班�?\\n\\n            foreach (var personnel in context.AvailablePersonnel)\\n            {\\n                // 按日期分组该人员的任�?\\n                var dailyGroups = context.Tasks\\n                    .Where(t => decisionVars.ContainsKey((t.Id, personnel.Id)))\\n                    .GroupBy(t => t.WorkOrderDate.Date);\\n\\n                foreach (var dailyGroup in dailyGroups)\\n                {\\n                    var dailyTasks = dailyGroup.ToList();\\n                    \\n                    var constraint = solver.MakeConstraint(0, maxDailyHours, \\n                        $\\\"daily_workload_{personnel.Id}_{dailyGroup.Key:yyyyMMdd}\\\");\\n\\n                    foreach (var task in dailyTasks)\\n                    {\\n                        var estimatedHours = task.EstimatedHours ?? 1.0m; // 默认1小时\\n                        constraint.SetCoefficient(decisionVars[(task.Id, personnel.Id)], (double)estimatedHours);\\n                    }\\n                    constraintCount++;\\n                }\\n            }\\n\\n            _logger.LogDebug(\\\"�?日工作量约束构建完成 - 约束数量：{WorkloadConstraintCount}\\\", constraintCount);\\n            return constraintCount;\\n        }\\n\\n        #endregion\\n\\n        #region Level 3 业务约束 - 对应遗传算法业务逻辑验证\\n\\n        /// <summary>\\n        /// 添加Level 3业务约束 - 对应遗传算法的ExecuteLevel3BusinessLogicValidation\\n        /// 约束内容：FL优先级规则、人员技能匹配、项目连续性约�?\\n        /// 约束特点：业务规则严格执行，允许一定弹性但必须符合核心业务逻辑\\n        /// </summary>\\n        private async Task AddLevel3BusinessConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
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{"timestamp":"2025-09-22T13:07:27.658Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_9WnoyfBu63hGVKEiTSzN7lRH","output":"{\"output\":\"        /// <summary>\\n        /// 添加Level 3业务约束 - 对应遗传算法的ExecuteLevel3BusinessLogicValidation\\n        /// 约束内容：FL优先级规则、人员技能匹配、项目连续性约�?\\n        /// 约束特点：业务规则严格执行，允许一定弹性但必须符合核心业务逻辑\\n        /// </summary>\\n        private async Task AddLevel3BusinessConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            await Task.CompletedTask;\\n            var constraintCount = 0;\\n            \\n            _logger.LogDebug(\\\"💼 开始添加Level 3业务约束...\\\");\\n\\n            // 业务约束1：FL优先级规�?- 对应CalculateFLPriorityRuleScore\\n            //constraintCount += AddFLPriorityConstraints(solver, decisionVars, auxiliaryVars, context);\\n\\n            // 业务约束2：人员技能匹配约�?- 对应CalculateSkillMatchingConstraintScore\\n            //constraintCount += AddSkillMatchingConstraints(solver, decisionVars, auxiliaryVars, context);\\n\\n            // 业务约束3：项目连续性约�?- 对应CalculateProjectContinuityConstraintScore\\n            //constraintCount += AddProjectContinuityConstraints(solver, decisionVars, auxiliaryVars, context);\\n\\n            // 业务约束4：人员资质约�?- 确保只分配给具备相应资质的人�?\\n            //constraintCount += await AddQualificationConstraints(solver, decisionVars, context);\\n            \\n            // 业务约束5：【Ultra think】人员班次不可用性硬约束 - 确保不会分配给不可用人员\\n            //constraintCount += await AddPersonnelUnavailabilityConstraints(solver, decisionVars, context);\\n            \\n            _logger.LogDebug(\\\"�?Level 3业务约束添加完成 - 约束数量：{ConstraintCount}\\\", constraintCount);\\n        }\\n\\n        /// <summary>\\n        /// 添加FL优先级约�?- 对应遗传算法的FL优先级规则验�?\\n        /// 规则10：优先分配本项目FL人员\\n        /// </summary>\\n        private int AddFLPriorityConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            Dictionary<string, Variable> auxiliaryVars,\\n            GlobalAllocationContext context)\\n        {\\n            var constraintCount = 0;\\n\\n            foreach (var task in context.Tasks)\\n            {\\n                // 获取该任务关联的FL人员\\n                var taskFLPersonnel = task.WorkOrderFLPersonnels?\\n                    .Select(fl => fl.FLPersonnelId)\\n                    .Where(flId => context.AvailablePersonnel.Any(p => p.Id == flId))\\n                    .ToList() ?? new List<long>();\\n\\n                if (taskFLPersonnel.Any())\\n                {\\n                    // 创建FL优先约束：优先分配给FL人员，如果分配给非FL人员则记录违�?\\n                    var flViolationVarKey = $\\\"fl_violation_{task.Id}\\\";\\n                    if (auxiliaryVars.ContainsKey(flViolationVarKey))\\n                    {\\n                        var constraint = solver.MakeConstraint(0, double.PositiveInfinity, $\\\"fl_priority_{task.Id}\\\");\\n                        \\n                        // FL人员分配变量\\n                        foreach (var flPersonnelId in taskFLPersonnel)\\n                        {\\n                            if (decisionVars.ContainsKey((task.Id, flPersonnelId)))\\n                            {\\n                                constraint.SetCoefficient(decisionVars[(task.Id, flPersonnelId)], 1);\\n                            }\\n                        }\\n                        \\n                        // 非FL人员分配变量\\n                        var nonFLPersonnel = context.AvailablePersonnel\\n                            .Where(p => !taskFLPersonnel.Contains(p.Id))\\n                            .ToList();\\n                            \\n                        foreach (var nonFL in nonFLPersonnel)\\n                        {\\n                            if (decisionVars.ContainsKey((task.Id, nonFL.Id)))\\n                            {\\n                                constraint.SetCoefficient(decisionVars[(task.Id, nonFL.Id)], -1);\\n                            }\\n                        }\\n                        \\n                        // 违规指示变量\\n                        constraint.SetCoefficient(auxiliaryVars[flViolationVarKey], 1);\\n                        constraintCount++;\\n                    }\\n                }\\n            }\\n\\n            _logger.LogDebug(\\\"�?FL优先级约束构建完�?- 约束数量：{FLConstraintCount}\\\", constraintCount);\\n            return constraintCount;\\n        }\\n\\n        /// <summary>\\n        /// 【重构简化】技能匹配约�?- 基于预筛选结果的轻量级实�?\\n        /// 【架构优化】：移除复杂的多源资质逻辑，依赖预筛选阶段的专业验证\\n        /// 【职责分离】：技能匹配交由ContextBuilderEngine处理，这里仅做约束\\n[... omitted 4 of 260 lines ...]\\n\\n人员参与指示变�?\\n                var projectPersonnelVars = new Dictionary<long, Variable>();\\n                foreach (var personnel in context.AvailablePersonnel)\\n                {\\n                    var participationVar = solver.MakeBoolVar($\\\"project_participation_{projectGroup.Key}_{personnel.Id}\\\");\\n                    projectPersonnelVars[personnel.Id] = participationVar;\\n                    \\n                    // 如果人员参与项目，则至少分配一个任�?\\n                    var constraint = solver.MakeConstraint(0, double.PositiveInfinity, \\n                        $\\\"project_min_tasks_{projectGroup.Key}_{personnel.Id}\\\");\\n                    \\n                    constraint.SetCoefficient(participationVar, -1);\\n                    \\n                    foreach (var task in projectTasks)\\n                    {\\n                        if (decisionVars.ContainsKey((task.Id, personnel.Id)))\\n                        {\\n                            constraint.SetCoefficient(decisionVars[(task.Id, personnel.Id)], 1);\\n                        }\\n                    }\\n                    constraintCount++;\\n                }\\n                \\n                // 限制项目参与人员总数\\n                var personnelLimitConstraint = solver.MakeConstraint(0, maxProjectPersonnel, \\n                    $\\\"project_personnel_limit_{projectGroup.Key}\\\");\\n                \\n                foreach (var kvp in projectPersonnelVars)\\n                {\\n                    personnelLimitConstraint.SetCoefficient(kvp.Value, 1);\\n                }\\n                constraintCount++;\\n            }\\n\\n            _logger.LogDebug(\\\"�?项目连续性约束构建完�?- 约束数量：{ContinuityConstraintCount}\\\", constraintCount);\\n            return constraintCount;\\n        }\\n\\n        /// <summary>\\n        /// 【重构优化】添加人员资质约�?- 基于ContextBuilderEngine的成熟实�?\\n        /// 【架构改进】：复用预构建的预筛选结果，避免重复的资质验证逻辑\\n        /// 【性能优化】：利用PrefilterResults缓存，实现O(1)查询复杂�?\\n        /// 【一致性保证】：与ContextBuilderEngine使用完全相同的资质验证标�?\\n        /// </summary>\\n        private async Task<int> AddQualificationConstraints(\\n            Solver solver,\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\n            GlobalAllocationContext context)\\n        {\\n            var constraintCount = 0;\\n            \\n            _logger.LogDebug(\\\"🔍 开始构建资质约�?- 基于预筛选结果的高效实现\\\");\\n\\n            foreach (var kvp in decisionVars)\\n            {\\n                var (taskId, personnelId) = kvp.Key;\\n                var variable = kvp.Value;\\n                \\n                // 【核心改进】直接使用预筛选结果中的资质验证结�?\\n                var prefilterKey = $\\\"{taskId}_{personnelId}\\\";\\n                \\n                if (context.PrefilterResults.TryGetValue(prefilterKey, out var prefilterResult))\\n                {\\n                    // 检查预筛选结果中是否包含资质相关的违�?\\n                    var hasQualificationViolation = prefilterResult.ConstraintViolations\\n                        .Any(violation => violation.Contains(\\\"资质\\\") || violation.Contains(\\\"qualification\\\"));\\n                    \\n                    if (hasQualificationViolation)\\n                    {\\n                        // 硬约束：预筛选发现资质违规，禁止此分�?\\n                        var constraint = solver.MakeConstraint(0, 0, $\\\"prefilter_qualification_{taskId}_{personnelId}\\\");\\n                        constraint.SetCoefficient(variable, 1);\\n                        constraintCount++;\\n                        \\n                        _logger.LogTrace(\\\"🚫 资质约束应用 - 任务{TaskId}人员{PersonnelId}：{Violations}\\\", \\n                            taskId, personnelId, string.Join(\\\"; \\\", prefilterResult.ConstraintViolations.Take(2)));\\n                    }\\n                }\\n                else\\n                {\\n                    // 【安全保障】无预筛选数据时的后备验�?\\n                    var isQualified = await CheckQualificationCompatibilityAsync(taskId, personnelId, context);\\n                    if (!isQualified)\\n                    {\\n                        var constraint = solver.MakeConstraint(0, 0, $\\\"fallback_qualification_{taskId}_{personnelId}\\\");\\n                        constraint.SetCoefficient(variable, 1);\\n                        constraintCount++;\\n                        \\n                        _logger.LogTrace(\\\"🚫 后备资质约束应用 - 任务{TaskId}人员{PersonnelId}\\\", taskId, personnelId);\\n                    }\\n                }\\n            }\\n\\n            _logger.LogInformation(\\\"�?资质约束构建完成 - 约束数量：{QualificationConstraintCount}（基于预筛选结果）\\\", constraintCount);\\n            return constraintCount;\\n        }\\n\\n        #endregion\\n\\n        #region 公平性约�?- 对应遗传算法负载均衡机制\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
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{"timestamp":"2025-09-22T13:07:30.288Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Inspecting fairness constraints code**"}}
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{"timestamp":"2025-09-22T13:07:31.139Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Inspecting fairness constraints code**"}],"content":null,"encrypted_content":"gAAAAABo0UoT2RObhUftcq5SXHg7W58vlupaXo6tWmOJxHqWxEeSifxRvOlMGeaYauB92BnIhW3ZBOOF2114GF_d72rIg3e5ZPoOVPSdq4R8LD6JKlg9EZnWWjuEeWblOAI3waoOlLqW7Yut5n8qAjH8ay4NYaLEeyPzxturJHKr90_yn0H7I22kzXdaeZaahZjJIgvtV0b2L-TSHV8rf9e3Zsy20_oWhXNICVMuPLoFkPa9WTnqIjDTOFXyMXoq9TTmhq8CJgsUU7kMqSlJHCpVZi8tDzrAy-wqy85tk0LU1K0G5g6AFMJ3_SZ8AZ9w60PxKDnKNAArBFANVhtHqjA2EtDYgxGGQnNr1j0pn3QZY8rTrlYKqmyYnXfaBO4tY-rBW_1welVNlrANKdebN2lgK70Z_VKOvKudrkmF6R84_xReAmi7pCnnbs9xG1Zifth1qPH_k4n8vuAGNCGUbVhgn3-yJNdkCbtUo6gNIMhe48H5z_phLPRW3mLkLt4Dbb874tplvTOQnfkCYVrgC7r8XUc3R0sv8ELYjlXXhetp9EX_Ih092YlxWvT159vEhgvr_CfOvjOpjB_huG8wQ7jqV_04bnzhzxk5eg0oUOZDAK_RxciEjq7oIwndt5MMW_LLmmM5rtZxa3wt6az13IH0IOgSWg8gP7iYSIhSZDVyEldSCjKW343Nx45YDKg1l5gK8sj6lvzkADyDKgIs-I1souBkLDG6gWwhHdYZO09KhCCUlXmGanI4ZFQKF_hbwITm3HNnbdkElQ-zPI50DOpYMEBnt-s1HmzK89Fpmb7dL02gMSIbxSXrRLwxmS2Zy-U_duYZDmZ2ytPP77SaNKilMXPLRx_XiSWdK7z-KcfsQu6fW82UJS9NQww10-jE8xfGzEs8jJDval22XxRs7NtwqBcr9eEJS0ju35Xm0wK5_zINVggnjjHBRCph7A50yyPUREqzpZgGHWv-APf0yfTu90T7fonsQ2M3OoUB_2KyBHFv018Z9hISQZDXzfNh1Md38llDLMlyB5Q5vkOog1IiBxmPcFVS1L74fjnssGD8Tr-jdJdIDa0cjpcZlHQ-Ub-ZpSjpdrVgHBUQShVlUydF3OJS_A-0lTzuZ3rPAID7Q34zr-VIYvjzv5UjINxUFKw2DF0zIOlMyhIbhl4VwJP96ZvkSAA0fw=="}}
{"timestamp":"2025-09-22T13:07:31.139Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 830 -First 140\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_2m1sS91QCsMQ33SytvCd1lRi"}}
{"timestamp":"2025-09-22T13:07:31.139Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_2m1sS91QCsMQ33SytvCd1lRi","output":"{\"output\":\"        /// <summary>\\r\\n        /// 添加公平性约�?- 对应遗传算法的CalculateFairnessScore和负载均衡机�?\\r\\n        /// 包含：负载均衡约束、人员利用率约束、过度集中惩罚约�?\\r\\n        /// </summary>\\r\\n        private void AddFairnessConstraints(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            var constraintCount = 0;\\r\\n            \\r\\n            _logger.LogInformation(\\\"⚖️ 开始添加公平性约�?..\\\");\\r\\n\\r\\n            var totalTasks = context.Tasks.Count;\\r\\n            var totalPersonnel = context.AvailablePersonnel.Count;\\r\\n            var avgWorkload = (double)totalTasks / totalPersonnel;\\r\\n\\r\\n            // 公平性约�?：负载均衡偏差计�?\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                var workloadVarKey = $\\\"workload_{personnel.Id}\\\";\\r\\n                var posDevVarKey = $\\\"pos_dev_{personnel.Id}\\\";\\r\\n                var negDevVarKey = $\\\"neg_dev_{personnel.Id}\\\";\\r\\n\\r\\n                if (auxiliaryVars.ContainsKey(workloadVarKey) && \\r\\n                    auxiliaryVars.ContainsKey(posDevVarKey) && \\r\\n                    auxiliaryVars.ContainsKey(negDevVarKey))\\r\\n                {\\r\\n                    // 约束：workload = avgWorkload + pos_deviation - neg_deviation\\r\\n                    var balanceConstraint = solver.MakeConstraint(avgWorkload, avgWorkload, \\r\\n                        $\\\"load_balance_{personnel.Id}\\\");\\r\\n                    \\r\\n                    balanceConstraint.SetCoefficient(auxiliaryVars[workloadVarKey], 1);\\r\\n                    balanceConstraint.SetCoefficient(auxiliaryVars[posDevVarKey], -1);\\r\\n                    balanceConstraint.SetCoefficient(auxiliaryVars[negDevVarKey], 1);\\r\\n                    constraintCount++;\\r\\n                }\\r\\n            }\\r\\n\\r\\n            // 公平性约�?：最大负载限�?- 防止极端不均�?\\r\\n            var maxReasonableWorkload = Math.Ceiling(avgWorkload * 1.5);\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                var workloadVarKey = $\\\"workload_{personnel.Id}\\\";\\r\\n                if (auxiliaryVars.ContainsKey(workloadVarKey))\\r\\n                {\\r\\n                    var constraint = solver.MakeConstraint(0, maxReasonableWorkload, \\r\\n                        $\\\"max_workload_{personnel.Id}\\\");\\r\\n                    constraint.SetCoefficient(auxiliaryVars[workloadVarKey], 1);\\r\\n                    constraintCount++;\\r\\n                }\\r\\n            }\\r\\n\\r\\n            // 公平性约�?：最小人员利用率约束 - 确保不会过度集中\\r\\n            var minPersonnelUtilization = totalTasks > 10 ? Math.Max(3, totalPersonnel * 0.4) : 2;\\r\\n            \\r\\n            // 创建人员参与指示变量\\r\\n            var participationVars = new List<Variable>();\\r\\n            foreach (var personnel in context.AvailablePersonnel)\\r\\n            {\\r\\n                var participationVar = solver.MakeBoolVar($\\\"participation_{personnel.Id}\\\");\\r\\n                participationVars.Add(participationVar);\\r\\n                \\r\\n                // 如果人员参与，则至少分配1个任�?\\r\\n                var constraint = solver.MakeConstraint(0, double.PositiveInfinity, \\r\\n                    $\\\"participation_min_{personnel.Id}\\\");\\r\\n                \\r\\n                constraint.SetCoefficient(participationVar, -1);\\r\\n                \\r\\n                var personnelTasks = decisionVars.Where(v => v.Key.PersonnelId == personnel.Id);\\r\\n                foreach (var kvp in personnelTasks)\\r\\n                {\\r\\n                    constraint.SetCoefficient(kvp.Value, 1);\\r\\n                }\\r\\n                constraintCount++;\\r\\n            }\\r\\n            \\r\\n            // 最小参与人数约�?\\r\\n            var minParticipationConstraint = solver.MakeConstraint(minPersonnelUtilization, double.PositiveInfinity, \\r\\n                \\\"min_personnel_utilization\\\");\\r\\n            \\r\\n            foreach (var participationVar in participationVars)\\r\\n            {\\r\\n                minParticipationConstraint.SetCoefficient(participationVar, 1);\\r\\n            }\\r\\n            constraintCount++;\\r\\n\\r\\n            _logger.LogDebug(\\\"�?公平性约束添加完�?- 约束数量：{ConstraintCount}，平均负载：{AvgWorkload:F2}，最大负载：{MaxWorkload:F2}，最小参与人数：{MinParticipation}\\\", \\r\\n                constraintCount, avgWorkload, maxReasonableWorkload, minPersonnelUtilization);\\r\\n        }\\r\\n\\r\\n        #endregion\\r\\n\\r\\n        #region 目标函数 - 对应遗传算法适应度函�?\\r\\n\\r\\n        /// <summary>\\r\\n        /// 设置增强版目标函�?- 对应遗传算法的多维度适应度评�?\\r\\n        /// 目标组成：基础评分 + 约束惩罚 + 公平性奖�?+ 业务优先级奖�?\\r\\n        /// 权重设计：约束满�?最�? > 公平�?�? > 业务优化(�? > 其他因素(�?\\r\\n        /// </summary>\\r\\n        private void SetEnhancedObjectiveFunction(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            var objective = solver.Objective();\\r\\n            objective.SetMaximization();\\r\\n            \\r\\n            _logger.LogInformation(\\\"🎯 构建增强版多维度目标函数...\\\");\\r\\n\\r\\n            // 目标组件1：基础分配评分 - 基于预筛选结果的质量评分\\r\\n            AddBaseAssignmentScore(objective, decisionVars, context);\\r\\n\\r\\n            // 目标组件2：约束违规惩�?- 对应遗传算法的约束评�?\\r\\n            AddConstraintViolationPenalties(objective, auxiliaryVars, context);\\r\\n\\r\\n            // 目标组件3：公平性奖�?- 对应遗传算法的公平性评�?\\r\\n            AddFairnessRewards(objective, auxiliaryVars, context);\\r\\n\\r\\n            // 目标组件4：业务优先级奖励 - 对应遗传算法的业务逻辑优化\\r\\n            //AddBusinessPriorityRewards(objective, decisionVars, auxiliaryVars, context);\\r\\n\\r\\n            _logger.LogInformation(\\\"�?增强版目标函数构建完成，包含4大评分维�?);\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 添加基础分配评分 - 基于预筛选结果和任务-人员匹配质量\\r\\n        /// </summary>\\r\\n        private void AddBaseAssignmentScore(\\r\\n            Objective objective,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            foreach (var kvp in decisionVars)\\r\\n            {\\r\\n                var (taskId, personnelId) = kvp.Key;\\r\\n                var variable = kvp.Value;\\r\\n                \\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
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{"timestamp":"2025-09-22T13:07:33.418Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Continuing method implementation**"}}
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{"timestamp":"2025-09-22T13:07:34.366Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Continuing method implementation**"}],"content":null,"encrypted_content":"gAAAAABo0UoWrPBY529d5uG75qnK4-p-bjDdIMm7F-Z02Bs4U_CsFaLTQFqPbVJCo8otMfX4WawYWDdpbQzmsPuYL0ZSL3wonbCtpncrimFax52XDF5-crvNGD3oq2EpHLAOAhbWByd3KEEhlSjk8fhBXG9r5wjlwO9m_4J0xnivwQremFFzBEmW3kR2yedjXB8BjKflRpo_K3QF7BsJ5PsornsmqvFGCz88f9U4DQwgB2NQtoHf_yJJNlN4NmdgK1IvQwzqMo-XrcSNa81L6VxAZe3Mdok9VDPCP_Ss2JhlWivR3d265HRA7gVcwCxNYs2C9rsaq3-mMHCiSyGh_SOzcLzWBKk5SeBzoaQGl30e_j5A0GCuTcs9IA-9PbPfobPecAtm_sm9i0KrfUZrMEgZn55PDqGmcvJEXqsNEgaQwybAt0dpi1pJD27b4S2FYXztz37Lp-cZNsOjXN7PzQO8XDP4tXFe23dxvWnZ8y1Sl4Wrs3pomV__ajuR5-lEUpQcIUaK7tmoCjsA5IGLf8tPokDsIKTEHQzDXwJLiQrOBSIkmlCUgJdf39aPSeDfwsdQeh_d08Mw2MYdbUUFmT1j3bMDokA15kLQDbjaRRDppIEKYs3nPQTVPK15-ZJAXi1fclCiSVSMtBCBgUu8L0zjVi8bPyeW7Bw8zTVzLCZHvk51oBqao0gOKYHkxdEkHiPyHkqpwOYJIpDcZM3jpKV8wmeRD_JVj_RBx1l2yz0Td-TxOKRvdDsWeLneAhPS59nOTj6mdm1s_6slAkK17MJtyTNfXbuMoEglxfhXRbEjHaQErR4Q6lEKrEgqqVG3bBINdau29BtxW3cmqCqpImMYSVv2vMB0MlZbCBP-FzV0cq-gkeT8pL0WYzsbRXgAqBa0xgsYVQrW"}}
{"timestamp":"2025-09-22T13:07:34.366Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 920 -First 200\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_0fNfUQhKfaCiJVoWIvtDQV1D"}}
{"timestamp":"2025-09-22T13:07:34.366Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_0fNfUQhKfaCiJVoWIvtDQV1D","output":"{\"output\":\"        }\\r\\n\\r\\n        #endregion\\r\\n\\r\\n        #region 目标函数 - 对应遗传算法适应度函�?\\r\\n\\r\\n        /// <summary>\\r\\n        /// 设置增强版目标函�?- 对应遗传算法的多维度适应度评�?\\r\\n        /// 目标组成：基础评分 + 约束惩罚 + 公平性奖�?+ 业务优先级奖�?\\r\\n        /// 权重设计：约束满�?最�? > 公平�?�? > 业务优化(�? > 其他因素(�?\\r\\n        /// </summary>\\r\\n        private void SetEnhancedObjectiveFunction(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            var objective = solver.Objective();\\r\\n            objective.SetMaximization();\\r\\n            \\r\\n            _logger.LogInformation(\\\"🎯 构建增强版多维度目标函数...\\\");\\r\\n\\r\\n            // 目标组件1：基础分配评分 - 基于预筛选结果的质量评分\\r\\n            AddBaseAssignmentScore(objective, decisionVars, context);\\r\\n\\r\\n            // 目标组件2：约束违规惩�?- 对应遗传算法的约束评�?\\r\\n            AddConstraintViolationPenalties(objective, auxiliaryVars, context);\\r\\n\\r\\n            // 目标组件3：公平性奖�?- 对应遗传算法的公平性评�?\\r\\n            AddFairnessRewards(objective, auxiliaryVars, context);\\r\\n\\r\\n            // 目标组件4：业务优先级奖励 - 对应遗传算法的业务逻辑优化\\r\\n            //AddBusinessPriorityRewards(objective, decisionVars, auxiliaryVars, context);\\r\\n\\r\\n            _logger.LogInformation(\\\"�?增强版目标函数构建完成，包含4大评分维�?);\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 添加基础分配评分 - 基于预筛选结果和任务-人员匹配质量\\r\\n        /// </summary>\\r\\n        private void AddBaseAssignmentScore(\\r\\n            Objective objective,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            foreach (var kvp in decisionVars)\\r\\n            {\\r\\n                var (taskId, personnelId) = kvp.Key;\\r\\n                var variable = kvp.Value;\\r\\n                \\r\\n                // 基础评分：基于预筛选结�?\\r\\n                var prefilterKey = $\\\"{taskId}_{personnelId}\\\";\\r\\n                var baseScore = 100.0; // 默认基础�?\\r\\n                \\r\\n                if (context.PrefilterResults.TryGetValue(prefilterKey, out var prefilterResult))\\r\\n                {\\r\\n                    baseScore = Math.Max(baseScore, prefilterResult.PrefilterScore * 2); // 放大预筛选评分的影响\\r\\n                }\\r\\n                \\r\\n                // 技能匹配奖�?\\r\\n                var task = context.Tasks.FirstOrDefault(t => t.Id == taskId);\\r\\n                if (task != null)\\r\\n                {\\r\\n                    var skillBonus = CalculateSkillMatchingBonus(task, personnelId, context);\\r\\n                    baseScore += skillBonus;\\r\\n                }\\r\\n                \\r\\n                objective.SetCoefficient(variable, baseScore);\\r\\n            }\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 添加约束违规惩罚 - 对应遗传算法的三级约束惩罚机�?\\r\\n        /// </summary>\\r\\n        private void AddConstraintViolationPenalties(\\r\\n            Objective objective,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            // Level 1惩罚：时间冲突严厉惩�?\\r\\n            foreach (var kvp in auxiliaryVars.Where(v => v.Key.StartsWith(\\\"conflict_\\\")))\\r\\n            {\\r\\n                objective.SetCoefficient(kvp.Value, -LEVEL1_CONSTRAINT_PENALTY);\\r\\n            }\\r\\n\\r\\n            // Level 2惩罚：班次规则违规重度惩�?\\r\\n            // foreach (var kvp in auxiliaryVars.Where(v => v.Key.StartsWith(\\\"shift_violation_\\\")))\\r\\n            // {\\r\\n            //     objective.SetCoefficient(kvp.Value, -LEVEL2_CONSTRAINT_PENALTY);\\r\\n            // }\\r\\n\\r\\n            // Level 3惩罚：业务逻辑违规中度惩罚\\r\\n            // foreach (var kvp in auxiliaryVars.Where(v => v.Key.StartsWith(\\\"fl_violation_\\\")))\\r\\n            // {\\r\\n            //     objective.SetCoefficient(kvp.Value, -LEVEL3_CONSTRAINT_PENALTY);\\r\\n            // }\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 添加公平性奖�?- 对应遗传算法的负载均衡评�?\\r\\n        /// </summary>\\r\\n        private void AddFairnessRewards(\\r\\n            Objective objective,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            // 负载均衡奖励：减少偏差获得奖�?\\r\\n            // foreach (var kvp in auxiliaryVars.Where(v => v.Key.StartsWith(\\\"pos_dev_\\\") || v.Key.StartsWith(\\\"neg_dev_\\\")))\\r\\n            // {\\r\\n            //     // 偏差越小，奖励越多（通过负的偏差系数实现�?\\r\\n            //     objective.SetCoefficient(kvp.Value, -FAIRNESS_REWARD_FACTOR);\\r\\n            // }\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 添加业务优先级奖�?- 对应遗传算法的业务规则优�?\\r\\n        /// </summary>\\r\\n        private void AddBusinessPriorityRewards(\\r\\n            Objective objective,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            // FL优先级奖�?\\r\\n            foreach (var task in context.Tasks)\\r\\n            {\\r\\n                var taskFLPersonnel = task.WorkOrderFLPersonnels?\\r\\n                    .Select(fl => fl.FLPersonnelId)\\r\\n                    .Where(flId => context.AvailablePersonnel.Any(p => p.Id == flId))\\r\\n                    .ToList() ?? new List<long>();\\r\\n\\r\\n                if (taskFLPersonnel.Any())\\r\\n                {\\r\\n                    foreach (var flPersonnelId in taskFLPersonnel)\\r\\n                    {\\r\\n                        if (decisionVars.ContainsKey((task.Id, flPersonnelId)))\\r\\n                        {\\r\\n                            // FL人员执行本项目任务获得奖�?\\r\\n                            objective.SetCoefficient(decisionVars[(task.Id, flPersonnelId)], 500.0);\\r\\n                        }\\r\\n                    }\\r\\n                }\\r\\n            }\\r\\n\\r\\n            // 任务优先级和紧急度奖励\\r\\n            foreach (var task in context.Tasks)\\r\\n            {\\r\\n                var priorityBonus = task.Priority * 50.0;\\r\\n                var urgencyBonus = task.Urgency * 30.0;\\r\\n                var totalBonus = priorityBonus + urgencyBonus;\\r\\n\\r\\n                var taskVariables = decisionVars.Where(v => v.Key.TaskId == task.Id);\\r\\n                foreach (var kvp in taskVariables)\\r\\n                {\\r\\n                    objective.SetCoefficient(kvp.Value, totalBonus);\\r\\n                }\\r\\n            }\\r\\n        }\\r\\n\\r\\n        #endregion\\r\\n\\r\\n        #region 结果处理和验�?\\r\\n\\r\\n        /// <summary>\\r\\n        /// 处理增强版求解器结果 - 包含完整的解析和验证逻辑\\r\\n        /// </summary>\\r\\n        private async Task<GlobalOptimizedSolution> ProcessEnhancedSolverResult(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context,\\r\\n            Solver.ResultStatus resultStatus,\\r\\n            long elapsedMs)\\r\\n        {\\r\\n            var solution = new GlobalOptimizedSolution\\r\\n            {\\r\\n                ActualGenerations = 1 // 线性规划一次求�?\\r\\n            };\\r\\n\\r\\n            _logger.LogInformation(\\\"📊 开始解析增强版线性规划结�?- 状态：{Status}，耗时：{ElapsedMs}ms\\\", \\r\\n                resultStatus, elapsedMs);\\r\\n\\r\\n            if (resultStatus == Solver.ResultStatus.OPTIMAL || resultStatus == Solver.ResultStatus.FEASIBLE)\\r\\n            {\\r\\n                var assignments = new Dictionary<long, long>();\\r\\n                var workloadDistribution = new Dictionary<long, decimal>();\\r\\n                var totalScore = 0.0;\\r\\n                var constraintViolations = new List<string>();\\r\\n\\r\\n                // 解析任务分配结果\\r\\n                foreach (var kvp in decisionVars)\\r\\n                {\\r\\n                    if (kvp.Value.SolutionValue() > 0.5) // 布尔变量阈�?\\r\\n                    {\\r\\n                        var (taskId, personnelId) = kvp.Key;\\r\\n                        assignments[taskId] = personnelId;\\r\\n                        \\r\\n                        workloadDistribution[personnelId] = workloadDistribution.GetValueOrDefault(personnelId, 0) + 1;\\r\\n                        \\r\\n                        // 计算分配评分\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
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{"timestamp":"2025-09-22T13:07:42.079Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Reviewing solver penalty and reward usage**"}}
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{"timestamp":"2025-09-22T13:07:43.790Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Reviewing solver penalty and reward usage**"}],"content":null,"encrypted_content":"gAAAAABo0UofTFuxZ35DqZV65HdnjNqibTlut09y-me-9KMv8elNK2lAcgG_GOUHiaON-Zc-MdthabFX1EbiDWPVk4pekHCEydcYF88zE8_Yl0TtcD0DJ8HO486Pro7gEGB5zAV8DzvybqEhUUkHIXPWSztFYmR5E69vcVIL-CM0I-pdyn2xvwSsXlTHcdlm0dTECdB_S9Qj-1e4EFImx-t0G00xnye5WtzB8vfa1o2nB5NVMYgTsskir2lgxM6Dbb8e-InqBRZgOanD5C1xH-lKf4fd6aCxeZd-QvByzvwvYC5jYx_zXvKoov2J-5CuvppZIpuyAWJTkLDu5vPiOL9_WXQnhyD6nYVgnHMCr67rMTNIC0ltCnMbrNXX6vx3zirIkRqnYUtP4TwdACMHCK7LfHSBhXJFTAjz4NgqlKlniyE1q6Hqb50Y7LfyrjaL2-2mcXh86oqP33QsHZlpAi1FN_bBTbq9rCz2q5bUc3qYLImyTr8cF3DV7qNPBGhvSqKYA-jHIfvLVcti4I8uilqLvxvroiff1O0Ec0RgPrerut1l7EIYMwxsetHdg-mcj6n62130K5og43-5UlCNjuYNldy15fJCwGQq-aalbUB2QBsz9wkPssp7nL1yL7PDkX3m1M4AwIUuoBESKcPyPKqH_DOGnaw1QIxSyq9h3QpOGdNqElg380Lvwh7yXLnkMVkvsSqJEKqO50YUrRgznSkAMdtt-ZXhWctgsvLnRvaz_yAIvRqjq4hZQX-T1PAdS9erh2wHi9H-d9VgqmMXSVRnxSrJX2uOFBy42OpusjVsVmCO0XGix98kxUooElmzCKDctzcKIIfqSXbSJe1NJ4SirJ9bSYp5BRf5CJZjROMiSZ9JYAipg01LC5d4SIPBcD7W80tjvKRJz9hccsb1erUVLKehst8NZ0KaBAQCI41ayr6znY63_7oKBEknZZrBvNH6O1VtfRCEFvZq1gIBnGitNs1W3gFe8IMdw6zdStqHveTNL8X9Ux2md-CD1W1HAX3D2m8kullWqALVraYv7-lRP5PyvuYt00kY55tZIRC5uFqSNEPw1HtuW5ThzwplVIQTTM9k2aGFhp-bLpEolvYPG89y3ZVF7qL1cF7jqIE76O1QVr4-G-dRAjPDRl9njTQ-tC8IFOiPtcOhDbMI1UCdmJjqXA2tc-cL2zzceTXa2Z48Ida5c3rqcLn48Jy50BvcLiuT6d8TSYDdM8KIUElV_thsdsIhjPAnj9j-Kgw8K0ee1igl01lUfdTDFOGXjg1q4auTwyvn5OM4S8Ow7bWYApI-figrtyyXrVRNSkXO-wv_aKxHsC92G8DgpknmGA3ItXfRQlNWI4BCXdvdT3sr48OrlQ5FkVTo_L07eldHj1i2BtPUVdh-p2RjuQaXonfy_N1KpjnEtOxdbx9ombbMIQjS2doPkTf8q76_uvHip5Phh8yEdtgiveBdr3o8vnZfWVgB8BHfTmSbRUmZsLmD8_J2lh9ygYSE1bEfAso_8lsinlATbWoIQ6ZAOFHcqj9jMG6BXXjcZE3M0v5EW2yJtYBHQP2nlD5SsCpCSjcXcyT-aI6PcEhOeV29G9z46k-7SA9zx_kVXEqSc7jME-5lPeAUXZhYoD7szbKuhV4cRKKrp8DQb1KHiUowhc6Qx0aQY5_WV6LoXikgtULuWeKyz3NlGxlGrCPfkG-HBfowvdwCD99XiK3xf0m6cztuwOfbOYs64l877uUts6I0JIDg13d_yIvfjtRP5_mj5ufULMK7UivM1zs_VYUhJud5VE4RicCbza5LmD8agd-y5Me7kStsJBuQC03t3LVrPqT09l7eLBWAd7OcZeC_MugWh495mz6SEcM5Ogjmw6ZQJ7cxQYGHddvHRM-8wNuxjV_UQP8DaXW-nzlEZQWriiJYY2I9mjpyAS1qez2StdzwiFWeWJ8ADIEZmol5QGV8HVExVw2a8IjqO9pyaPGiQiBA5O2VYbzZ-wXotWaS0b_nvhc0rqKNjinaKWIhzd-CUVHPgi7TehA9P5aRqSGeXvakeoSWV5BHhBf6iXhKthU4k5F-X7l1_vZs5ujwqphQLR1_LRnsEFBgnPZttSScSaMoKOKJcjxFLpTbStzcY16NcM4lxy7oAvocmZC4AX_ewkQDhUxl-YSB0CEJQw6AI5xOR3OvIbAxQs2e9FNbVcc3n7VKkhSKSv0h2KDkgE-IHIDKkiYUxxEsgAt90HA5eEKmozDr5rguNlV7n8HKCZZicrmYnoY2YHjAnpwVuBT0ZH0a1thFD6A1Ipk6YEwQ3IW_KmS2jJNhG-pOxLwaEzYuA96xQeLJ3FzbRz6ziI4WhPy_tmidVemz5SkYetdkpYRr5yKRU8FWZmhxrlVOxi4xoR687o5a7AcdC79njz8wH15Pn4TSuUjXVJGzcNkHh_Je1xRGROJWzOSvapATBpBK-PUu9G9SSSZakppyn8oDnbz9MVYu8DO4mz1dTSJAa7FWvtf8KWSub1HEXMs47rQhNwLaMk0TYqyg7MANYKIoGmQiavON1VtYO1xgO7zn_gd1RDOIvDVX3qvwsbmNGX9pXu8HUUwgCCpjxidxM9dbb_IMZPqkjbkNQ8mFP3f5VQySTvaUaaN1DGrwjI64L78x5peYEP67i_pRCoKdEgX71aPcSMM7r53zlP2WtJwV1Q5JKBXvlhpbGT3g6paIxxy9dVhU5-6Wm-_4ybN7UD4OZFvQqKqymLwEOLk4B8QCQFV9uGoqmsnlBm5g3y-MJfIMOTRdrpM8Nj_t7l11kcFSDODOQH89fmlJb_RoBYBH7SgKDMIIlqmE2u3f"}}
{"timestamp":"2025-09-22T13:07:43.790Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 1080 -First 200\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_6xjg9d4b9KIVIR2tHM5EoHBl"}}
{"timestamp":"2025-09-22T13:07:43.790Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_6xjg9d4b9KIVIR2tHM5EoHBl","output":"{\"output\":\"\\r\\n        #region 结果处理和验�?\\r\\n\\r\\n        /// <summary>\\r\\n        /// 处理增强版求解器结果 - 包含完整的解析和验证逻辑\\r\\n        /// </summary>\\r\\n        private async Task<GlobalOptimizedSolution> ProcessEnhancedSolverResult(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context,\\r\\n            Solver.ResultStatus resultStatus,\\r\\n            long elapsedMs)\\r\\n        {\\r\\n            var solution = new GlobalOptimizedSolution\\r\\n            {\\r\\n                ActualGenerations = 1 // 线性规划一次求�?\\r\\n            };\\r\\n\\r\\n            _logger.LogInformation(\\\"📊 开始解析增强版线性规划结�?- 状态：{Status}，耗时：{ElapsedMs}ms\\\", \\r\\n                resultStatus, elapsedMs);\\r\\n\\r\\n            if (resultStatus == Solver.ResultStatus.OPTIMAL || resultStatus == Solver.ResultStatus.FEASIBLE)\\r\\n            {\\r\\n                var assignments = new Dictionary<long, long>();\\r\\n                var workloadDistribution = new Dictionary<long, decimal>();\\r\\n                var totalScore = 0.0;\\r\\n                var constraintViolations = new List<string>();\\r\\n\\r\\n                // 解析任务分配结果\\r\\n                foreach (var kvp in decisionVars)\\r\\n                {\\r\\n                    if (kvp.Value.SolutionValue() > 0.5) // 布尔变量阈�?\\r\\n                    {\\r\\n                        var (taskId, personnelId) = kvp.Key;\\r\\n                        assignments[taskId] = personnelId;\\r\\n                        \\r\\n                        workloadDistribution[personnelId] = workloadDistribution.GetValueOrDefault(personnelId, 0) + 1;\\r\\n                        \\r\\n                        // 计算分配评分\\r\\n                        var assignmentScore = CalculateAssignmentScore(taskId, personnelId, context);\\r\\n                        totalScore += assignmentScore;\\r\\n                    }\\r\\n                }\\r\\n\\r\\n                // 验证解的质量\\r\\n                var qualityMetrics = await ValidateSolutionQuality(assignments, auxiliaryVars, context);\\r\\n                \\r\\n                solution.BestSolution = assignments;\\r\\n                solution.BestFitness = solver.Objective().Value();\\r\\n                solution.PersonnelWorkloadDistribution = workloadDistribution;\\r\\n                solution.ConstraintSatisfactionRate = qualityMetrics.ConstraintSatisfactionRate;\\r\\n                // solution.QualityMetrics = qualityMetrics; // 暂时注释，等待扩展GlobalOptimizedSolution\\r\\n\\r\\n                _logger.LogInformation(\\\"�?增强版线性规划求解成�?- 目标值：{ObjectiveValue:F2}，分配任务：{TaskCount}，使用人员：{PersonnelCount}，约束满足率：{SatisfactionRate:P2}\\\",\\r\\n                    solution.BestFitness, assignments.Count, workloadDistribution.Count, qualityMetrics.ConstraintSatisfactionRate);\\r\\n\\r\\n                // 详细的工作负载分�?\\r\\n                LogWorkloadAnalysis(workloadDistribution, context.Tasks.Count, context.AvailablePersonnel.Count);\\r\\n            }\\r\\n            else\\r\\n            {\\r\\n                _logger.LogWarning(\\\"⚠️ 增强版线性规划无�?- 状态：{Status}，可能原因：约束过于严格\\\", resultStatus);\\r\\n                solution.BestSolution = new Dictionary<long, long>();\\r\\n                solution.BestFitness = 0.0;\\r\\n                solution.ConstraintSatisfactionRate = 0.0;\\r\\n                // solution.ErrorMessage = $\\\"线性规划模型无解，状态：{resultStatus}\\\"; // 暂时注释，等待扩展GlobalOptimizedSolution\\r\\n            }\\r\\n\\r\\n            return solution;\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 验证解的质量 - 对应遗传算法的约束满足率计算\\r\\n        /// </summary>\\r\\n        private async Task<SolutionQualityMetrics> ValidateSolutionQuality(\\r\\n            Dictionary<long, long> assignments, \\r\\n            Dictionary<string, Variable> auxiliaryVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            await Task.CompletedTask;\\r\\n            \\r\\n            var metrics = new SolutionQualityMetrics();\\r\\n            var violationCount = 0;\\r\\n            var totalChecks = 0;\\r\\n\\r\\n            // 检�?：任务分配完整�?\\r\\n            var completenessRate = assignments.Count > 0 ? (double)assignments.Count / context.Tasks.Count : 0.0;\\r\\n            metrics.TaskCompletenessRate = completenessRate;\\r\\n            if (completenessRate < 1.0) violationCount++;\\r\\n            totalChecks++;\\r\\n\\r\\n            // 检�?：时间冲突验�?\\r\\n            var timeConflictCount = 0;\\r\\n            var personnelTimeSlots = assignments.GroupBy(a => a.Value)\\r\\n                .ToDictionary(g => g.Key, g => g.Select(kvp => \\r\\n                {\\r\\n                    var task = context.Tasks.First(t => t.Id == kvp.Key);\\r\\n                    return new { Task = task, Date = task.WorkOrderDate.Date, ShiftId = task.ShiftId };\\r\\n                }).ToList());\\r\\n\\r\\n            foreach (var kvp in personnelTimeSlots)\\r\\n            {\\r\\n                var timeGroups = kvp.Value.GroupBy(t => new { t.Date, t.ShiftId });\\r\\n                foreach (var timeGroup in timeGroups)\\r\\n                {\\r\\n                    if (timeGroup.Count() > 1)\\r\\n                    {\\r\\n                        timeConflictCount += timeGroup.Count() - 1;\\r\\n                    }\\r\\n                }\\r\\n                totalChecks++;\\r\\n            }\\r\\n\\r\\n            metrics.TimeConflictRate = totalChecks > 0 ? (double)timeConflictCount / totalChecks : 0.0;\\r\\n            if (timeConflictCount > 0) violationCount++;\\r\\n\\r\\n            // 检�?：负载均衡评�?\\r\\n            if (assignments.Any())\\r\\n            {\\r\\n                var workloadValues = personnelTimeSlots.Values.Select(v => (decimal)v.Count).ToList();\\r\\n                var giniCoefficient = CalculateGiniCoefficient(workloadValues);\\r\\n                metrics.LoadBalanceScore = Math.Max(0.0, 1.0 - giniCoefficient);\\r\\n                \\r\\n                var utilizationRate = (double)personnelTimeSlots.Count / context.AvailablePersonnel.Count;\\r\\n                metrics.PersonnelUtilizationRate = utilizationRate;\\r\\n            }\\r\\n\\r\\n            // 综合约束满足�?\\r\\n            metrics.ConstraintSatisfactionRate = totalChecks > 0 ? \\r\\n                Math.Max(0.0, 1.0 - (double)violationCount / totalChecks) : 1.0;\\r\\n\\r\\n            return metrics;\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 记录详细的工作负载分析日�?\\r\\n        /// </summary>\\r\\n        private void LogWorkloadAnalysis(Dictionary<long, decimal> workloadDistribution, int totalTasks, int totalPersonnel)\\r\\n        {\\r\\n            if (!workloadDistribution.Any()) return;\\r\\n\\r\\n            var workloads = workloadDistribution.Values.ToList();\\r\\n            var avgWorkload = workloads.Average(w => (double)w);\\r\\n            var maxWorkload = workloads.Max();\\r\\n            var minWorkload = workloads.Min();\\r\\n            var utilizationRate = (double)workloadDistribution.Count / totalPersonnel;\\r\\n\\r\\n            _logger.LogInformation(\\\"📈 工作负载分析 - 平均：{Avg:F2}，最大：{Max}，最小：{Min}，人员利用率：{Util:P2}，基尼系数：{Gini:F4}\\\", \\r\\n                avgWorkload, maxWorkload, minWorkload, utilizationRate, CalculateGiniCoefficient(workloads));\\r\\n\\r\\n            // 详细分布统计\\r\\n            var distributionLog = string.Join(\\\", \\\", \\r\\n                workloadDistribution.OrderBy(kv => kv.Key)\\r\\n                                  .Select(kv => $\\\"人员{kv.Key}:{kv.Value}任务\\\"));\\r\\n            \\r\\n            _logger.LogDebug(\\\"📊 详细负载分布：{Distribution}\\\", distributionLog);\\r\\n        }\\r\\n\\r\\n        #endregion\\r\\n\\r\\n        #region ShiftRuleValidationEngine集成辅助方法\\r\\n\\r\\n        /// <summary>\\r\\n        /// 【深度实现】构建ShiftRuleValidationEngine所需的验证上下文\\r\\n        /// 【业务思考】：模拟当前批次中其他可能的人员分配，为班次规则验证提供完整的上下文信息\\r\\n        /// 【技术难点】：需要从决策变量中推断出可能的分配结果，构建验证所需的WorkOrderEntity列表\\r\\n        /// </summary>\\r\\n        private List<WorkOrderEntity> BuildValidationContext(\\r\\n            long targetPersonnelId, \\r\\n            long targetTaskId, \\r\\n            GlobalAllocationContext context,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars)\\r\\n        {\\r\\n            var validationContext = new List<WorkOrderEntity>();\\r\\n            \\r\\n            try\\r\\n            {\\r\\n                // 【上下文构建策略1】添加同一人员的其他高概率分配任务\\r\\n                // 基于预筛选结果找到该人员其他可能的任务分�?\\r\\n                foreach (var task in context.Tasks.Where(t => t.Id != targetTaskId))\\r\\n                {\\r\\n                    var prefilterKey = $\\\"{task.Id}_{targetPersonnelId}\\\";\\r\\n                    if (context.PrefilterResults.TryGetValue(prefilterKey, out var prefilterResult))\\r\\n                    {\\r\\n                        // 高评分或标记为可行的任务加入验证上下�?\\r\\n                        if (prefilterResult.IsFeasible || prefilterResult.PrefilterScore > 70.0)\\r\\n                        {\\r\\n                            var contextTask = task.CloneForValidation();\\r\\n                            contextTask.AssignedPersonnelId = targetPersonnelId; // 模拟分配\\r\\n                            validationContext.Add(contextTask);\\r\\n                        }\\r\\n                    }\\r\\n                }\\r\\n                \\r\\n                // 【上下文构建策略2】添加已确定分配的任务（来自历史数据�?\\r\\n                context.PersonnelHistoryTasks.TryGetValue(targetPersonnelId, out List<WorkOrderHistoryItem> historyTasks);\\r\\n                var existingAssignments = historyTasks.Where(t =>\\r\\n                        t.TaskId != targetTaskId)\\r\\n                    .ToList();\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:07:43.790Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:08:35.827Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Highlighting modeling flaws and potential bugs**\n\nI'm reviewing numerous potential issues in the solution's modeling, like missing null or empty checks that cause divide-by-zero errors, fairness constraints that might not enforce intended outcomes due to missing objectives, grouping tasks only by date (not shift/time) causing unrealistic conflicts, and contradictions in minimum personnel utilization that can make the solver unsatisfiable. Many parts like dynamic shift constraints are defined but unused, and some constraints’ logic could lead to degenerate or unsolvable conditions. There is a lot here suggesting the model needs careful fixing to avoid silent logical errors and infeasible problems."}}
{"timestamp":"2025-09-22T13:08:36.926Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":316712,"cached_input_tokens":286848,"output_tokens":5440,"reasoning_output_tokens":3968,"total_tokens":322152},"last_token_usage":{"input_tokens":33556,"cached_input_tokens":30976,"output_tokens":2193,"reasoning_output_tokens":2112,"total_tokens":35749},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:08:36.926Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Highlighting modeling flaws and potential bugs**\n\nI'm reviewing numerous potential issues in the solution's modeling, like missing null or empty checks that cause divide-by-zero errors, fairness constraints that might not enforce intended outcomes due to missing objectives, grouping tasks only by date (not shift/time) causing unrealistic conflicts, and contradictions in minimum personnel utilization that can make the solver unsatisfiable. Many parts like dynamic shift constraints are defined but unused, and some constraints’ logic could lead to degenerate or unsolvable conditions. There is a lot here suggesting the model needs careful fixing to avoid silent logical errors and infeasible problems."}],"content":null,"encrypted_content":"[encoded data, 13644 characters]"}}
{"timestamp":"2025-09-22T13:08:36.926Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -Skip 2180 -First 120\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_Wq9Gnjb73O0p62S8mPVhI9eu"}}
{"timestamp":"2025-09-22T13:08:36.926Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Wq9Gnjb73O0p62S8mPVhI9eu","output":"{\"output\":\"\\r\\n            return constraintCount;\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 【Ultra think核心】添加人员班次不可用性硬约束\\r\\n        /// 【业务逻辑】：确保线性规划模型不会将任务分配给在该时间段不可用的人员\\r\\n        /// 【约束策略】：硬约�?- 直接禁止不可用分配，不允许任何违�?\\r\\n        /// 【数据来源】：DateShiftUnavailablePersonnel缓存 + PersonnelUnavailabilityIndex索引\\r\\n        /// 【性能优化】：仅对已创建决策变量的组合进行约束，避免无效约�?\\r\\n        /// </summary>\\r\\n        private async Task<int> AddPersonnelUnavailabilityConstraints(\\r\\n            Solver solver,\\r\\n            Dictionary<(long TaskId, long PersonnelId), Variable> decisionVars,\\r\\n            GlobalAllocationContext context)\\r\\n        {\\r\\n            var constraintCount = 0;\\r\\n            \\r\\n            try\\r\\n            {\\r\\n                _logger.LogDebug(\\\"🚫 开始构建人员班次不可用性硬约束\\\");\\r\\n                \\r\\n                var startTime = DateTime.Now;\\r\\n                var processedVariables = 0;\\r\\n                var constrainedVariables = 0;\\r\\n\\r\\n                // 【Ultra think策略】：对所有决策变量检查不可用性约�?\\r\\n                foreach (var kvp in decisionVars)\\r\\n                {\\r\\n                    var (taskId, personnelId) = kvp.Key;\\r\\n                    var variable = kvp.Value;\\r\\n                    processedVariables++;\\r\\n\\r\\n                    // 查找对应的任务实�?\\r\\n                    var task = context.Tasks.FirstOrDefault(t => t.Id == taskId);\\r\\n                    if (task == null) continue;\\r\\n\\r\\n                    // 【核心检查】：使用多重策略检查人员不可用�?\\r\\n                    var isUnavailable = await IsPersonnelUnavailableForTaskAsync(task, personnelId, context);\\r\\n                    \\r\\n                    if (isUnavailable)\\r\\n                    {\\r\\n                        // 【硬约束】：强制该决策变量为0 (不允许分�?\\r\\n                        var constraint = solver.MakeConstraint(0, 0, $\\\"unavailable_{taskId}_{personnelId}\\\");\\r\\n                        constraint.SetCoefficient(variable, 1);\\r\\n                        constraintCount++;\\r\\n                        constrainedVariables++;\\r\\n                        \\r\\n                        _logger.LogTrace(\\\"🚫 应用不可用性约�?- 任务{TaskId}人员{PersonnelId}在{Date}班次{ShiftId}不可�?, \\r\\n                            taskId, personnelId, task.WorkOrderDate.ToString(\\\"yyyy-MM-dd\\\"), task.ShiftId);\\r\\n                    }\\r\\n                }\\r\\n\\r\\n                var elapsedMs = (DateTime.Now - startTime).TotalMilliseconds;\\r\\n                \\r\\n                _logger.LogInformation(\\\"�?人员班次不可用性约束构建完�?- 耗时：{ElapsedMs}ms，处理变量：{ProcessedCount}，约束变量：{ConstrainedCount}，约束数量：{ConstraintCount}，约束率：{ConstraintRate:P2}\\\",\\r\\n                    elapsedMs, processedVariables, constrainedVariables, constraintCount, \\r\\n                    processedVariables > 0 ? (double)constrainedVariables / processedVariables : 0);\\r\\n\\r\\n                await Task.CompletedTask;\\r\\n                return constraintCount;\\r\\n            }\\r\\n            catch (Exception ex)\\r\\n            {\\r\\n                _logger.LogError(ex, \\\"💥 人员班次不可用性约束构建异�?);\\r\\n                throw;\\r\\n            }\\r\\n        }\\r\\n\\r\\n        #endregion\\r\\n\\r\\n        #region 人员不可用性检查辅助方�?\\r\\n\\r\\n        /// <summary>\\r\\n        /// 【Ultra think方法】检查人员在指定任务时间是否不可用（同步版本�?\\r\\n        /// 【高性能设计】：优先使用缓存索引，提供快速决策能�?\\r\\n        /// 【多重验证】：DateShiftUnavailablePersonnel + PersonnelUnavailabilityIndex双重验证\\r\\n        /// </summary>\\r\\n        private bool IsPersonnelUnavailableForTask(WorkOrderEntity task, long personnelId, GlobalAllocationContext context)\\r\\n        {\\r\\n            try\\r\\n            {\\r\\n                // 【验证策�?】使用DateShiftUnavailablePersonnel快速检�?\\r\\n                var dateShiftKey = $\\\"{task.WorkOrderDate:yyyy-MM-dd}_{task.ShiftId}\\\";\\r\\n                if (context.DateShiftUnavailablePersonnel.TryGetValue(dateShiftKey, out var unavailablePersonnel))\\r\\n                {\\r\\n                    if (unavailablePersonnel.Contains(personnelId))\\r\\n                    {\\r\\n                        return true; // 该人员在此日期班次不可用\\r\\n                    }\\r\\n                }\\r\\n\\r\\n                // 【验证策�?】使用PersonnelUnavailabilityIndex进行人员维度检�?\\r\\n                if (context.PersonnelUnavailabilityIndex.TryGetValue(personnelId, out var unavailabilityIndex))\\r\\n                {\\r\\n                    var taskDate = task.WorkOrderDate.Date;\\r\\n                    if (unavailabilityIndex.TryGetValue(taskDate, out var unavailableShifts))\\r\\n                    {\\r\\n                        if (task.ShiftId.HasValue && unavailableShifts.Contains(task.ShiftId.Value))\\r\\n                        {\\r\\n                            return true; // 人员在该日期的该班次不可�?\\r\\n                        }\\r\\n                    }\\r\\n                }\\r\\n                \\r\\n                // 检查历史任务人员的分配\\r\\n                if (context.PersonnelHistoryTasks.TryGetValue(personnelId, out var historyTask))\\r\\n                {\\r\\n                    var taskDate = task.WorkOrderDate.Date;\\r\\n\\r\\n                    if (historyTask.Any(m => m.WorkDate.Date == taskDate))\\r\\n                    {\\r\\n                        return true;\\r\\n                    }\\r\\n                }\\r\\n                \\r\\n                return false; // 人员可用\\r\\n            }\\r\\n            catch (Exception ex)\\r\\n            {\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:08:36.926Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:08:53.416Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Summarizing class analysis and issues**\n\nI’m gathering key observations about the LinearProgrammingEngine class—its commented-out features, critical bugs in fairness constraints, division-by-zero risks, and potential logical flaws in task assignment constraints. The file’s size and complexity raise maintainability concerns, so I’m noting line references and planning to confirm interactions with skill matching next while preparing to update the analysis plan accordingly."}}
{"timestamp":"2025-09-22T13:08:53.622Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":353875,"cached_input_tokens":322432,"output_tokens":6215,"reasoning_output_tokens":4672,"total_tokens":360090},"last_token_usage":{"input_tokens":37163,"cached_input_tokens":35584,"output_tokens":775,"reasoning_output_tokens":704,"total_tokens":37938},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:08:53.622Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Summarizing class analysis and issues**\n\nI’m gathering key observations about the LinearProgrammingEngine class—its commented-out features, critical bugs in fairness constraints, division-by-zero risks, and potential logical flaws in task assignment constraints. The file’s size and complexity raise maintainability concerns, so I’m noting line references and planning to confirm interactions with skill matching next while preparing to update the analysis plan accordingly."}],"content":null,"encrypted_content":"[encoded data, 5752 characters]"}}
{"timestamp":"2025-09-22T13:08:53.622Z","type":"response_item","payload":{"type":"function_call","name":"update_plan","arguments":"{\"plan\":[{\"status\":\"completed\",\"step\":\"Skim class layout to map major responsibilities\"},{\"status\":\"completed\",\"step\":\"Review critical methods for modeling, constraint setup, and objective\"},{\"status\":\"in_progress\",\"step\":\"Summarize findings, risks, and improvement ideas\"}]}","call_id":"call_ODdSFqN5mONpzbYkOAgitISM"}}
{"timestamp":"2025-09-22T13:08:53.623Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_ODdSFqN5mONpzbYkOAgitISM","output":"Plan updated"}}
{"timestamp":"2025-09-22T13:08:53.623Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:03.457Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing structured final summary message**"}}
{"timestamp":"2025-09-22T13:09:04.577Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":391873,"cached_input_tokens":360320,"output_tokens":6611,"reasoning_output_tokens":4992,"total_tokens":398484},"last_token_usage":{"input_tokens":37998,"cached_input_tokens":37888,"output_tokens":396,"reasoning_output_tokens":320,"total_tokens":38394},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:04.577Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Preparing structured final summary message**"}],"content":null,"encrypted_content":"gAAAAABo0UpwWQvGz04KEftGdUf1em0ujyNMvDEefhjhoNGngz7geI_JyKzDgBHiFhJsUMI1sF5LJW9S7Ev85hSkECj9JsW9BJ7gr-7tF3Ve0goRoIlsDVzm5LrsnBtQpHN09MLFYvVxyYWQZAp2odxBwl9DZJlDCqMinaLV9l4g6qjqZ3nPBmd4sXA5nN7ml6up6haretuvXRY3aUJ4SgoGujArbIOk3s0ILmclWBlW4I_XEYDrDMESAOoGySLkLseM30_exUHKvrytnBHLrzDWd4bamU05T5lh_SvKr3uihk9hNt_yzaX8hICnLbbitW2IQBKilU3vIhfNMhqaS_ar2Ji05GhPErH5Ti2JsomMhQ36TSm0_yOYF3d5heLcjh2hoz1rqmhMyLJwhLqRGYOLPc4LaRR1wvDHyqkN88vTYoFaPNlsNV6qsHXXNr_ct0gldRsqhj4BvkyHJ5M7S5hQkIbd711t04cuinkrMiX2AqEsSeU8_HWwd0NuQas5CsXwR7q1y0j9HDyOfaZAuv3GheN6J9iFbes_q8lF5PAdq5xQ4WTVdVmYzangovDF7S-GyEfsV263fo0uTh3gkm6Hs4gEmmHqFg4xVHo6bALIsxShrp5q2wj0pt-pcj-8NXFJvT_xvTjzjMuyU235ty5uihX4N8wPSlqOADl8hj6TKcM38MNoiRBdOMXipKmNByGeS0pwigZq6z-MFPmqdPSkpExPEKn3zwq1pW0EbJbND7OJa_PLS4qAT3o5Qj8IKOIj1EaAhu-7wrNizjGdyIEt4gV1CYz1ic_GD5RLvuue7uQ6V6fJPdix66H83Fx7-GEMXdcS9Y37rEzJpZcf0BY4mJ6FVSGo8xKG9Uw4lyU4wP6e-ZOdBC8CEjQ1mrZku6cohRYFYOtlCSBkLJ_Rch-_kdUNwd-sCDylcMj7TX6YZQ3sgwsg5zJeo8XKa277Rb_RDcW8dZHgGDVczHpkQMuIjPnDKkZjd4nD_pP_4-90lj_R3o8xqhrZBY4_lBxsgOpJ0IXw3lnBZhWCbeKjzEDT5zja88oVYC4fdCOXeB-kTDR5-RI4a2SOZ56PrstpGOjMuq-TsdVioFdKC7hppo2TFmoDTst18aMCxz9B7ukaBdgWnrFBeELLBSW_F6VzCDz1Cawt0-dWhg3WL5LcPAMkmHw_72-0-06KmoD3CZSw2UgdWYP9_n3zk8lnqVUzxXoq_6tqNH8qSbPRwYSkf8Hh_esSBsN84MCdwkx8WguubALX0u0EtRdC6GcMDyZobSvtV7-jAOYLdSKIWye9P2s6RRHCsbI1y98lC1iRtWP_Gx7Hg88LBdyIZ2UvJzG_Y0MnkMH43pjQ88nhs9NfQet6GeS-jnxQzGrXVqoYhNMRGMzTtZd8gsC2t2jE5avWdsvKXN20ueydl8WjHLCC0Obhr0xF_1mbRmCho_kNBTiXLumK1xsGzs_UiwrEtPLeFhDFk_WtOVGrqjkE3_MKzq_HqufoDK_QdCC4yopHOnMtI7KoSolN2IQGaU9kjK3qVvzbblpt9LXPcoKhxE8nlGnkt9X2giL4gfyplvciTfTxjuoZisc_k-3Zzcggmbu8Q2vAsGhiVVdY1PjAK8_7L5_YlghMTOsOQhBav-y8MFM4HMP9YCK183E6JQpjpUcFq7K2c6vouBINmQySMH8EqWqKjVQAbOhyqqYRZwUgh2V87g_tzjzzEWD757Lt8rwc93KEM2RqpfDCgDI_quvszwsyOuZBHrhCOsosjxkFtd_M-Yr31t_HrnMJtd9-GLPcDLlZjoXpqilDLVJMV8TGNkXISLhFcjOEZil8myitl13-5oPyF0kTz8LXDQbjbVgMgZZMGEX85yPs5TdNBw9i_JhbSJdT8NX2AODdflCO__PrUV1-dbSPzyb9eyDjP3LtE9yJT_8xy0uE78N4NPIPQKcfhrvscjXhMvx0hLB8AxxBDuYNFzgJmwCMwUiIGi981avub2BQ8zdbO1je8dcPTTzUtUjsZpixhFmTEXN6_M8vv-TTjV_2pKKZyzsnPKstOeASumVXv3K2oXmkw0ZPEiY84mBaXzgYTm8QDF6WjvDH4cF-FCm7dqDzmBhYB6ak5QxxudQUa5tkf2FfNzSxjryVLU5CXfioVYNADhPZWZkzo8PdZzZeywmIj6Vrqk3XodFV5zISHXrYgGC0vy9Wduk4-c93wBhgcYfoo_RjzZxF-ZVi1OpopkIUIr1a_r7i_QbyH9jZ8oRMvtQnP7YSIJp38DeBoiYMS8wWMkWy5eIeBrVgwoZVK1ztm0Z-q_4vnWnyxR49TZ10na5YOUj6NL73jhcCO5HWgfBGuuornRWCiaoO4rTlNrFlxMfiN1Y3uJKdK1dvG9tBCbpxp2c4EfQj8wUsksgecLfR7AIVDpZDjP49olQaW7j9R0WfredqWQHbXhQRVKp-6GFStuFtuDLTuQMRhY4Dh3DRaqGuOYwSE2Ll_N9fkiG_dBmj6lNWGQx1-EXYHQzbmjC1xcm3j4Vh5z9a49df42cFiHYtCARJMnGrKonrnOyevgy-cbSYYE6oIXZ_zsToCN9ZtnI-5fgI43hYCcPARqldlA0VPHfh23Y3rI8zlHzf0pNNWXhbZdXVf_JJHw93a48KP7n-0WTOwHGqTDH1t1eHyPCyqri322Dzh1T7pfcz8zelJBocEMisl7U-BrUFKVbDUjP89QZL_1GbT6GO1Msfy4PIG3hw6NjgOTGV7mw="}}
{"timestamp":"2025-09-22T13:09:04.577Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"minPersonnelUtilization\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_w1d8PgQJK4SGHghouTB3b6xJ"}}
{"timestamp":"2025-09-22T13:09:04.577Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_w1d8PgQJK4SGHghouTB3b6xJ","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:886:            var minPersonnelUtilization = totalTasks > 10 ? Math.Max(3, totalPersonn\\r\\nel * 0.4) : 2;\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:910:            var minParticipationConstraint = solver.MakeConstraint(minPersonnelUtili\\r\\nzation, double.PositiveInfinity, \\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:920:                constraintCount, avgWorkload, maxReasonableWorkload, minPersonnelUti\\r\\nlization);\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:04.577Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:08.266Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Locating code references with line numbers**"}}
{"timestamp":"2025-09-22T13:09:09.315Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":430456,"cached_input_tokens":398592,"output_tokens":6752,"reasoning_output_tokens":5056,"total_tokens":437208},"last_token_usage":{"input_tokens":38583,"cached_input_tokens":38272,"output_tokens":141,"reasoning_output_tokens":64,"total_tokens":38724},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:09.315Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Locating code references with line numbers**"}],"content":null,"encrypted_content":"gAAAAABo0Up1F-kZK3La7kX3wjdgorILkT_Lkz9exKe967JHhmwg0tbh5Vrh0lFt8Zx2d4rqyMtGYGuZu_npd15F8TTTWkvnzIvaBevHGYMck2_26iKd5BXDl848qbDM8BzzIcOqoUGMjFRoVphv7JyJOB--1SoEofVC_Cz0DMUhwAoTktj8YLbHMkCK4kKjt0zBI9FwhQ6uXsT_i22v85SECW6PGduluUHzMIBlmL9ntgUMG1bgmJdU0JSr6rqxtEBWCEn8fNXUVs3VUh2db_WZeq6riDcaTeywQd_cx3wPWYRmLk8yaea35R2WBtlgnliyZlRw-fERDJOj9LAxYoGK-NvpuaKqiPVQhbiwO7yE5geg6l0Z4pd3cw-qqy066hdl_Ol3dLYQzvMIdqeOhQB39VjENuvD5qO_IFJxB_6laX77JMIHnaA5TL5zgN-KTK58PFl3xk6udcrSzi08f4PmEL4CqYV9dTbzUz11Rk5bKxqoagCDRH0x5GzIJHy2gvtqBJcYc88KavJHH_kH_H0L2QdJaKoCHJM8NTEeMjRCNvodoWqqBwOGPKvTkqBcqbyVcKnl9rd7a5gPCGvjn6ailCHkjLZWZ8gErzhs-EdiV6kswkhS8f5dqYBeo9DAO4atMzH4jVH83nh0EJBdnJXd-sID21PVaMy2tMZh7YIvuHOzksSLCoZ9KbrGhnGxhZjtRIXVKt7Z97fnOkzdn6m8ousXvOsvoE5WuwdPkxXT4T1XYg65DqmpKf9WOlAOTqV3jN2uWc5J0b9i-QrWKiFy2qYlRTIBhXUXjWx-yV1FNyfCqoo0oMsRcZN6iqzpD8n4P4G5aZy_GKbOTjeILVCozHu3wpkJCL8rsXjadzO0obZj7jalANHhDIx7fX-D0BxOlNQ6QZea2FgZ55SmiKMsvAm9DGb-8ZanTiCGHVmr4Z8kyHI8g49t36FHVvf5rGDIdmE2ELO6s2HQ-_NJwv54Yj22ejL-mJBPf7vxOxMb6Jtx-z5H7Nl6x-dCroWlho0joOF2XIfySKcmK0cbSOXmWloWdUlTKtsW7przmVktvxvt6R9kL-jEYDgKrncKfJzKA6xJzuyd5YGhbHIWlVSOR2yjCb_W7h3ext_0-ZYtnlhJcZMvVYzZnFeA3IFaVdbMTlSE8_lykcql3a2DOp9MS0wN3IPj4SOC4gGUmDp9XnQoAEdbJSLQ0s6wJvaLQUhiJsGCKCVc1jDdSjM4T9i1tYP7fshLZVNogCmwJz5f0EGZxsfHXkabJM9UysM4XB39IcGVXXMfTmqsnedMzJPLWXQvlkCtk5UqhcprBCvjyArSF2eR0GwsGVoEG2B9CLJIC7Bou0lzO11qXFcm0cc7MEGyG54zZWGUiVP3GxVIhtrtONPTgO5Lkbt02adBOAEgQYqLy23WnXY85dTijRsklu45vhVAyg=="}}
{"timestamp":"2025-09-22T13:09:09.315Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"AddDynamicShiftRuleConstraints\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_2d9LFf7uoHHNDtgsBIxjYFjQ"}}
{"timestamp":"2025-09-22T13:09:09.315Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_2d9LFf7uoHHNDtgsBIxjYFjQ","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:393:            //constraintCount += await AddDynamicShiftRuleConstraints(solver, decisi\\r\\nonVars, auxiliaryVars, context);\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:407:        private async Task<int> AddDynamicShiftRuleConstraints(\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:09.315Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:22.467Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Noting comment addition**"}}
{"timestamp":"2025-09-22T13:09:24.401Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":469330,"cached_input_tokens":437248,"output_tokens":6829,"reasoning_output_tokens":5056,"total_tokens":476159},"last_token_usage":{"input_tokens":38874,"cached_input_tokens":38656,"output_tokens":77,"reasoning_output_tokens":0,"total_tokens":38951},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:24.401Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Noting comment addition**"}],"content":null,"encrypted_content":"gAAAAABo0UqDiucGTOJzsCRkizR_-dOqErhAIEAXW1TNnkBqN6eBFNMWY0TYym9NP0Pe9BybLh3oEXu4bk6kAUiw9z3fvL10NbDALgxB0EzNoZEZQhlUFoV8GCMixne0DsRJDZEl7PPMcj2wbbYpZdackf09N8GGfnEgOKssbb6MGGbdGk2d7AUQQY7UF2OZLHB4pAuflei84xrkeW5YuwxO-pPWzSDzEBHuslSEcjHI3r4O-PkqKWlJtFRnL2pfHotVbLxFXaNoXaVXppBIIh5tSqLfFDXE7po16F_LPCHwq1RckhvHKlWKGthIlPZr2TOkPA-gODMfUqumI_2D2AxEUBHcTiNITqDJrteoMJg_HqM8QHPGrfysAvIZQ7rEPbm9etzww5QyzAbVKrVD9l-irrqNyMTGJbG_N0X26dDllWgkSsB3bw9Zt2tPxh1K2qcSviZ0U_M3oweeCIw4XlwnDYzf9VWH9lgN_ZNzgjiyIFCQzfg70Uuyx8ln2XQOCRkt_l-Rrm3gqE-YQRzKemE2oTfWoFs6mZo8nf5Lkrw1t0g3ZIHbuIHRyaRZRA9biSu2fVVVqn5YVaKOIMzwkkd0PU8rXB6CEQossTO0UixiIpSekWqixWxfG8W56NMjZAJzLFtbVCre8M9w3Gehtuw5-hmFRW8_Ea4-LqRA3DB_Z0faPiQEgw2AbTiBO__AW395fW89mlHhaazN7wxQoJfAFwwvoTIYCxn27P5RIqltUz8lSbLiNk8G4bMIDQRPjLppWNhcV-X_JaXpn17I_vN4EM5JNX1wPhhCxgBmNLxIeVCTWkIVU2gWHWfWP2dkdrYUgVeYBi1RtneK2QHqFYSMEKremVVJbmJh3cei8NEjEIStg5RZNmB-tnFhKczgbjAylmfQExp6P_l_c1SMS1Uxk-0iG0-1RA=="}}
{"timestamp":"2025-09-22T13:09:24.401Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"AddDailyWorkloadConstraints\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_7Kp63NZfLtWIUGHkaoydAe6T"}}
{"timestamp":"2025-09-22T13:09:24.401Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_7Kp63NZfLtWIUGHkaoydAe6T","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:396:            //constraintCount += AddDailyWorkloadConstraints(solver, decisionVars, a\\r\\nuxiliaryVars, context);\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:531:        private int AddDailyWorkloadConstraints(\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:24.401Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:26.695Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Extending approach to Level3**"}}
{"timestamp":"2025-09-22T13:09:27.573Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":508407,"cached_input_tokens":476032,"output_tokens":6905,"reasoning_output_tokens":5056,"total_tokens":515312},"last_token_usage":{"input_tokens":39077,"cached_input_tokens":38784,"output_tokens":76,"reasoning_output_tokens":0,"total_tokens":39153},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:27.574Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Extending approach to Level3**"}],"content":null,"encrypted_content":"gAAAAABo0UqHzFGQ7nnTvdz7rqunoEuJwVZSoaysEnqNoBDCaHTUPCfZ-TWVaYPabsBgRab0QF5FIPhEVn4yx1CEFpFaglRI73GKMoGo9wOTLukhcuRlpFWHVBQjFGYurVL2q6VMQhaQiLKgRBbavhF8Tcyffkio5fS_QOXKSsiA5dkHSzIHazDPeQts7oWf8yXpuTWh6KIM1tU_zlYr5QXc7D6QZHXTqpUppviVqInEzWzaKbqkdXakpYdNuCsRXqV9GWRg0EULn84Nexkyc2joqx3KaC0Bb9HqhGpyXqbus1YXgL-rfbJu0Ml9BSke9064AJOVldLxC8C8dPVOiEhnctFo6rBQEA8_8oCzfA4g1JuduCKi7wlCA-MtzqcwUi099_5pd0XT5-XsOgv3lq3CVe26sOEd3_nyn9eJemokjpKhgrULD5ileyJQapfILPZyVvHQviaSp90ME6JHxWr_HtAAxZnmbxeZ1EryHVk3O1rszZXTwtTdrmtCQY8755JLu5wu1atRHObLLsGTYyKCo8sGERDY_nXGCk6MstAlhDzsTYW5VyF-54DTPorVcCMdALvYhL3PGNUbxShfO-eZJ2vv_xyHv1eMW-qQ7zsIBNTy2w-Z_BSffis72BCVMfFj5joT-IXF0CWhlOJIzolebnMp1WAsDpzGMzWi48gUPVH0SyY5gJqf1bygQRZHJgXIwv0nK1ZqkJV60ABm5ZXt_Xx53zRWjfEEAIIjzwD2Ar4ByrZM6YJaYhedJJ2Y5Q_qnD_EV_XD1X1m3kB7QyRVDAKLspVEHBrjvXFz9EI6FD6PFj-1De7MdgcFcVv3VYotjICJRZ1TCa2Hckg7q1SWxKVr8oqE-Q_5OkOw4cZmfSE0dKMLKwJ5H2LYV8s-Ji89vwSN9jrN"}}
{"timestamp":"2025-09-22T13:09:27.574Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"AddFLPriorityConstraints\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_4JSQ2oa3Zxxb3K5WzM86DNll"}}
{"timestamp":"2025-09-22T13:09:27.574Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_4JSQ2oa3Zxxb3K5WzM86DNll","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:588:            //constraintCount += AddFLPriorityConstraints(solver, decisionVars, auxi\\r\\nliaryVars, context);\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:609:        private int AddFLPriorityConstraints(\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:27.574Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:32.609Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Locating zero division risk**"}}
{"timestamp":"2025-09-22T13:09:33.721Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":547680,"cached_input_tokens":515072,"output_tokens":7046,"reasoning_output_tokens":5120,"total_tokens":554726},"last_token_usage":{"input_tokens":39273,"cached_input_tokens":39040,"output_tokens":141,"reasoning_output_tokens":64,"total_tokens":39414},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:33.721Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Locating zero division risk**"}],"content":null,"encrypted_content":"gAAAAABo0UqNS48MqIhGEqz1zcYRzIB5Mk_Psqree85UFrzwiX_lVrKvPwfmD0y7N_TiZWNmNH2m3uQB4yirX7SHWFoIaZuYI90KJjvu0dIT9TacV0hupKu3qdgwmHsylG7DGiEIRGMEM_yQnq5QWM4MjSpd_sSD4iDZwQ_8WDr8IXbWmgx0gxgfE7sDGRJR6wVAxY7Rx88ClMAr2pBPf8Gav_k6CqrOmM2wAk2rLFAvA7wO-3K7_vIIf1nYfKGT15Ie_-7rL0e0raSs1r9j28U0lER2AxXkED_SRW70KZl7fK1IIiF1XbSrt9Wfn_HFmSXhgwwNOf2cDiREYUtM_mItkyQq_VFQnWfWkha25NoOwxRR4iWVy4zVm8I9S0KHOW2fpU4UqUOMxG_xrZ_HWAV6CCcUMURid3xOi4eK7k63LrPrHudckq151M_sxKUS2fDDAtreKZRYbDEl0PzZnnqCbPgVToOYNdZCOvN5yU7ozR6kjSNSJoAHF5bzSnQhUQjOhsyCHxzFnGNQcRz8tNelOgTNF7YV9KQ5HYNkcqnHJ70KJ1TYWb68TP57B28w2RT8j2YZkBftcggn8uykals7ysrqH9quCLV-dlrAzxGxGDW9wBvjjFyrhXBk8C2DMvPjyfRSz3FfGRH1gCluzh3ZnjVGijLNmqUWuSJVNwoBFDKSsh0bD2UgsHmabF54UYHJeM6Mjm1x6rk06of030wbae84vRF4xKE51FPP71uBoZZAI11lzyoZMkBB5L1W034popqsGNVWd-2IvyzsPC7OaH3zaW1vDROT1baHafkT9j7NHg8oh8im0dkLXovQQILeF26E0fH1WQHFH0_xFkMuUgzwpro2_7vGnas0DT57RFMT9EM5-ZiHO0Fy4Y52OKP_JklsV7qebuWObmJHZE_Cx7MYGiXb9YeKPL2gaCNjmrEvUHDm9bcegSETNb1aWJ4jiaUy0tY5rM4EiGD7t0TbZJqnmGQXRbxKt8AtUG85EbbPHVgwSgKuV24x7GafXkgevw3qnrRYP_LIbTE-hFwofHvGgs6duJd8um3gPJUMXZrje_yproIqTPI4TQWY4-rMS4R4NCCJK2CnnHRaP8JDo9abYwtws5UwtPIOfuUik4LdhFzsccw-AcZGqe93_aQObO8zg4Dg4F5sTDWkY56L9MBEDOGTrQktz8kR31JjBB6rx544DXlh094yfoiKRQqGy0ExaRDPWelI_Yb7Tg64R_BeWKItzw=="}}
{"timestamp":"2025-09-22T13:09:33.722Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"var avgWorkload =\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_SlcqnUtFS1Wcm5XtRhpwu2Kj"}}
{"timestamp":"2025-09-22T13:09:33.722Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_SlcqnUtFS1Wcm5XtRhpwu2Kj","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:219:            var avgWorkload = (double)context.Tasks.Count / context.AvailablePersonn\\r\\nel.Count;\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:847:            var avgWorkload = (double)totalTasks / totalPersonnel;\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:1224:            var avgWorkload = workloads.Average(w => (double)w);\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:2156:            var avgWorkload = (double)totalTasks / totalPersonnel;\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:33.722Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:36.726Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Noting line numbers for date grouping**"}}
{"timestamp":"2025-09-22T13:09:37.860Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":587292,"cached_input_tokens":554368,"output_tokens":7137,"reasoning_output_tokens":5120,"total_tokens":594429},"last_token_usage":{"input_tokens":39612,"cached_input_tokens":39296,"output_tokens":91,"reasoning_output_tokens":0,"total_tokens":39703},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:37.860Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Noting line numbers for date grouping**"}],"content":null,"encrypted_content":"gAAAAABo0UqRG_Nd0g5TxU1HzOZUdnyJ9vsPTv22ZcAEzJiETBaw4QSxPZ5ShvQAos5bL-Rr92tEhJ8u3rSTlW-2f3FN_h2Z7jDHOgXUdKCiGjx0ZBCU4YynYoktLZC57TZtSKgipIfaEfOQjTsmVZnF1ZSAu576JMORLGSZ7GaRGT23K0POT3OrY4Oy7hkb0lHXYVy7t_N0SiKUrxQ6gLy4GYrpaCYdTUO-RliuB9jlF6U4Uf2ivDDHfGkma3CyvlfLSOOiqDlT6OWXzIrADUrPinzC0s_mKhxQiMEC4YWxBggJiay1kOdcuWALyYJ2py-oThicVKRyRGwKzXCmjDDNGRsC-9XkwvjQScOwemih5ZEKL2tcCJSyvVWvvQiFqvuB94i6cNqKAKCgbQdSBIfRfXR54eRevEOYTbFYujeWcvtG7rSY5cfp4hKR8eXoO5zN3CGdXH7PkrFO6BL6za0At7QBto_en5X5rLl9sYfFYhhvOxDB-po2B-VdpiSy4piTr9CBuNPMjb6PxNmRAMXnFk1CtafBY54o3_n9ZUJGU4Xzj0kKPXgmSBPNrbRQKknADrKYMtKm_8Ht7xsWByLaKtpXavHgW-0mCjXmQ9BrVWzoJaG8ChM3b56NML2bM2Y4Z3XNs7n7m8FsQPOIbvkrrsbFoQfios-OxNlW8bkqMSaLWTNtLjK-2JY338ddPqtwEdOyhlmddq74ekgGTqqIq-ivybV1SEioLv23GIiutgN6KJo5_YBc6_3h-ADsvtzZFU-DeSzWH5dlwMDr7dEXAXaK8W7Evr9dZ4guBuge7a2l8XAA3ryMGYJyS-ZOomLMtuTy9e3zz5QFyBfaGgNaUuyEvfqls5AF-WiZunheNDhPTzuakHfILLTi8t_mn47MomqvK73SAv__WPnJdbqe8GHFHHSJrShuKBEfi1PZeQbNC1FN4-ghAHi1yg-dlauZHTloo39FAsW6SK8-ajzWwPG-6oyklY9Vjvty2kA7-wMUmSzFmIwa1T4icG-BI9Cakj2v3YBYD4DNnBF2d2s3M5wqX08H_39M62tpooisvOTw-chrz-A6ETCsAfggVM7SHeiKIBcoc7nIH_ljQ2QfePxnqWwJYCZ49nTamq3W8NuemH7FuP5wZmlZFPKcTAec3bqKo5Kp"}}
{"timestamp":"2025-09-22T13:09:37.860Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"GroupBy\\\\(t => new \\\\{ Date = t.WorkOrderDate.Date \\\\}\\\\)\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_dwoFOoaHgSZ2fubqNjsBSZfs"}}
{"timestamp":"2025-09-22T13:09:37.860Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_dwoFOoaHgSZ2fubqNjsBSZfs","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:309:                var timeGroups = context.Tasks.GroupBy(t => new { Date = t.WorkOrder\\r\\nDate.Date });\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:37.861Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:40.928Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Locating workload fairness constraint**"}}
{"timestamp":"2025-09-22T13:09:41.891Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":627131,"cached_input_tokens":593920,"output_tokens":7216,"reasoning_output_tokens":5120,"total_tokens":634347},"last_token_usage":{"input_tokens":39839,"cached_input_tokens":39552,"output_tokens":79,"reasoning_output_tokens":0,"total_tokens":39918},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:41.891Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Locating workload fairness constraint**"}],"content":null,"encrypted_content":"gAAAAABo0UqWIsxtA_i67DOTv2ZQr8Rx2cnlyR69jJag_uLlr6TlEuTQ5ETjSUO0A99EZnBoZ8-GBK98pvd3uZHTJ1hIUh9T9OieKEjwEfa6E2tdceeNg6CVx3aYVqldsFcTOfjzBbOyvjoeXrf4y66S9BLlsHwHg68b2NFBwGV9JGqyGq4WseClLDyD6a8LC7NI2PUpCHj2zmY-LCKSs77wjOtaYxgv_NB-7rLE3WEflv0aoVy4S4Aoj8aiVLIdSkQrLjKDqvHguqtlyGS8ne0zDTt3rMRSPgRMuDN3sWPTEWWzoAB3s0qgunlmSgStvqRnZWfR1t_9KOVmXzaG5e5XlIAfOxm30m2r-wAuJ2H69RUugjA9C0haRkNVuWhU6SADbgAodUXCnT13qYgfDq7_Kquz0n9TqY-7Swm-FDGay8kQsNPNfGZSEPXdjMGs-IqgL08y4dx6DMOJGv2YgmjUOKWwE-NwUuIZQHiUl1x4UgFk7I8junwYXILXpXBSQOBBL_i1Ia7McPNnkzODCRGhLCUa8mhErrgkUPNuY4u-_yfvEgKHDpwJLxoFjnRFseHLmV63811yGIX1pDM5RuAhahngyaWo9DHtL49Q3EnvaY7BMO9NpAOxuof3a_6xhRjTJgOHtEAkB9LMz3hdDZrDKYJjPA9o9zF-tJH0iFliIgss7rvHKuBS__WMMXy3oavub6ENuS1Ipbh55QR0BNd1zDtL10ZNeq6zDqmDotWWWBky4QbQBZ2ogxhoIccW1bF7NR7O5nVLw5kuXRlc-AbsGuVuM4Kkd2NVJcBiEjU6g3FE8-BxxeOOnPt2ocMfw1HRyqUZAGkBks2mCZx4wrGIbHypDVCA9ovkY0-GhNTvC6fqgjfsbTXI4_7IKzAMUUHBwBRe5YBQ7VG3sBY4ajR0ag6uuEd7S1VMcKSH7IQFrn5xwEhaEpG3brO7Fzf8_WjO56zkWHczxXBtblMnwj2EhSctKaBl8W89M06b_F9pN7Wm14AluPBfVQjs-ATcRKoDeznC62h9h1DjVUeM5VgtFgRurmE5aiK-Qd-NwQVfG3qxTxNNSbQ="}}
{"timestamp":"2025-09-22T13:09:41.891Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"MakeConstraint\\\\(avgWorkload\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_UQ24QLuNkMfkCbj7VnblWeQh"}}
{"timestamp":"2025-09-22T13:09:41.891Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_UQ24QLuNkMfkCbj7VnblWeQh","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:861:                    var balanceConstraint = solver.MakeConstraint(avgWorkload, avgWo\\r\\nrkload, \\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:41.891Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:45.368Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Searching for fairness constraint comments**"}}
{"timestamp":"2025-09-22T13:09:46.174Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":667167,"cached_input_tokens":633728,"output_tokens":7292,"reasoning_output_tokens":5120,"total_tokens":674459},"last_token_usage":{"input_tokens":40036,"cached_input_tokens":39808,"output_tokens":76,"reasoning_output_tokens":0,"total_tokens":40112},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:46.174Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Searching for fairness constraint comments**"}],"content":null,"encrypted_content":"gAAAAABo0UqaZnF0bwKnlbtyGLoXSX-4arO2ILMxnAhUvoTe2rsoJXKBBLSpLlwmDAngFDauZqe1DbOWEqaGusx3vZqtriKJEke3qV5ezzNWvxtvtopWoPQR_QRAgjR5ZWMvj6KtN9Vk5kkWCKdnR1fTBjhS4M9Q9iQacjea_GuvZFjtb3E0RKtsrzuWtrkMunpuq7KDz5GAn134Dm4UtiX1ZZhXuRtGktf-hM8ccxGwIJo-978hsyuEnl05SSHmRD1vGIA9Qob1czaps50lPM7872DEOdUupAP1ESpfAKFn_KN0qX1ckkbN2uo6nz-kZoymgO8z0W5BafPLeNGUHWPuDHYpxtGbjIkfeN5jQkIfJl8md6SgAprT-bfJBnizVTv6s-acmOIz31EQ-jCaYZP_oxvQ-LLzhQItHVrDXTJQCyfa0qy168sSaQFLQTdfW4pnfYrnmQBtIcNpX4ekEBlcolXjMNO-9AIEfmEnnkD7m4htkJrKZbNaNh378PvsHq62p3ro8OSRToaqZNromeYCg7WNHiNjTIVPvz5v8k5VuscLK_pDmdzpyHxR77fgnn8jXHktfm6Wq82ZGgtahszNUgAGvV5dheMbNnmM8Edi0B8_BGhQw7h34t8PUeaQu7oMXhZtlk-w4C5UVnEC4kNRy_mefXtbNXxFu8kHQnFlDQQY5vOWrifSb-dpQIG8gEbNWzKrIdnITYBlGVLNLdAiavqK_p4GdH-xvqpQLDQRc_qP8pbYx642xn_MYz5ZDoK_qcIl-FcY9kKHiSADZPEvcoP0cro0BiVyfJTdROal_HmHK7c6-9XoyBRuIBfFHywXYe_k7rmMBTB6mqBLW5w0LDT35uzCOsh7c6vX9lyIf3VCbQnp100rvfBXVc9GT5JvdhGdVt806iJLee44IF6C5Gh7q845Kg8CZy7r1GbMeU6Gc09ruamVFDtI_s_GkZSvGd3IJPX6R3TV7sjAoztlbN_mBF6YlYE2w2f16OIbKEMDR32TRAPBbqLB2-5IQMNP9qr9nQdQlUIxlLewFUXoCOM4APVpR_DvURd8He1cKk4cg6vWTXpTKodmBTFrbY407qi2x25I"}}
{"timestamp":"2025-09-22T13:09:46.174Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"AddFairnessRewards\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_eUIRMwRBV57wE9iiCkONvAyt"}}
{"timestamp":"2025-09-22T13:09:46.174Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_eUIRMwRBV57wE9iiCkONvAyt","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:950:            AddFairnessRewards(objective, auxiliaryVars, context);\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:1022:        private void AddFairnessRewards(\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:46.175Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:49.456Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Reviewing internal comments and code references**"}}
{"timestamp":"2025-09-22T13:09:50.866Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":707420,"cached_input_tokens":673792,"output_tokens":7431,"reasoning_output_tokens":5184,"total_tokens":714851},"last_token_usage":{"input_tokens":40253,"cached_input_tokens":40064,"output_tokens":139,"reasoning_output_tokens":64,"total_tokens":40392},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:50.866Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Reviewing internal comments and code references**"}],"content":null,"encrypted_content":"gAAAAABo0UqepSnMjg6Y29HOmanrlaV_39CbgomPASOfJapifnevQHlL0R9ys1J2oG45sl8DRbL4rJPH2M58I7Tv4CFPydcSrELkBvMllw8kbM6ohrww5belI83Pcg5tRyTPt_T3dViQPowsYiLddOervQ3ztygrYZV-okuNQiHqYLqmy5eVQeetDj4EPpKCcW0nXrW4M9d3b28nJAAgXRW15cY6Fl5N47NB37E8BIbQK1TlYn0jqNe3riFkrhWZkelfHU0TX7N5ffZIJct2-_CPoRqurcDk4Mw-QdMacHzRmpz8CQGmi4Kf9_HSgnUB5ks8LvMRs9HJqeTa7eZTqqdlmuizQFD0EMVjVlYxsb83jc9jsrGr1ckjkjctQSChhr9G85qok_0fvfBSvzp1vPz71-qWHZHLB9XDfiekWgzoX1X-9gxBZAZOhU2ISTHpOFKQK1vcNdHBoFqV4-C3m6cYROz5nizshiIahY-fkXkOs7uNlj8-yZJxPoLj8Jk8UwB5DCUg3zxTLX1Uba0th2QUkwj1uocLpBEcBPGLlSOF49y9SN3VS2Lgd_0B0jDF1_8DWlKMAiJh59XHeKywEa2uXue3OJkqz8b702fC0kte1AYkZPcmQCgs7VjtYut9dygPR4ivDrhZUgaISxU8jo0X-aftRLPkaG6Y_2zw86Pi307MYD7OLzWFBS2ZrLoqJ4pAwPI8nFvYpI45EON736aPmAZdFqR54HSBJ0siaxY2e7PKsOrgJnOhhzzfhdqMdBsSwfqQ8hfWnmpZ4wX5BG8z5jGiSODPixfKOH_i3G-qjNT9w_jx-Z2pTsFn5tvLd2KhYftgMaA8qpGxl2WICL0OQnaOD2dTY1CiKpRlln-ee1z-CHKhHLgbNbfJJeJhEHBYebiZcfNuoEN6PmJcPdt5l3MfO2Zsv1pEsp-BRVOMcsDQUCG73Ht9qhCGCb8qtYE6ddzE8BO19SZC59Q9UBlR6OZr016MIiUIrGAzRvGbLwVDKQipEoq7VV_fORNBNt8d9mQ8uHWhisXBCPdl_mJtDJYqP5XRdxDkl6HHPB4nF5AxAqS_vQ-MQMfOkIeSuGLBYHb626MhwDyOJC5oe59cyoHgF3Y-DdLYEQYzcTZaEsVfL6fS_8PiJLXMd3YO_VykAjnp2obysaSWiEdO5V3Aj1IWtE8g0uMqANzaF-Yv-VR1Da-rqxYyTFWraLQsDvbjMs-mCrbM_u5mPDXBpaC6L3KoIR3Y1fBv-Y74q5Wcl4XuWjr0XWLlEyOhntxSYgcagn1n1HFvxrySY-jxI70lyuKtweTm3SC1bH21oEqT7Z8A-RcK1F8="}}
{"timestamp":"2025-09-22T13:09:50.866Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"pos_dev_\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_o5vnp6qV4oJkZOF4t6DvNCgP"}}
{"timestamp":"2025-09-22T13:09:50.866Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_o5vnp6qV4oJkZOF4t6DvNCgP","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:223:                var posDeviationVar = solver.MakeNumVar(0, context.Tasks.Count, $\\\"po\\r\\ns_dev_{personnel.Id}\\\");\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:224:                auxiliaryVars[$\\\"pos_dev_{personnel.Id}\\\"] = posDeviationVar;\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:853:                var posDevVarKey = $\\\"pos_dev_{personnel.Id}\\\";\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:1028:            // foreach (var kvp in auxiliaryVars.Where(v => v.Key.StartsWith(\\\"pos_d\\r\\nev_\\\") || v.Key.StartsWith(\\\"neg_dev_\\\")))\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:50.866Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:09:53.707Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Searching for code comments on shift_violation**"}}
{"timestamp":"2025-09-22T13:09:54.897Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":748058,"cached_input_tokens":714112,"output_tokens":7507,"reasoning_output_tokens":5184,"total_tokens":755565},"last_token_usage":{"input_tokens":40638,"cached_input_tokens":40320,"output_tokens":76,"reasoning_output_tokens":0,"total_tokens":40714},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:09:54.897Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Searching for code comments on shift_violation**"}],"content":null,"encrypted_content":"gAAAAABo0Uqia8yuqRjTNC9A-P76OO_i-7ROg_HWEM68iskeui29Tfgxc4GtqYwAQLj_Rne2pJNopImyRiUtr3z9FiVR5DzENIe1oClAqRU9AHL1K0Vy3ozkEimU_6Gh04SRShWOOsFcfTctZJBrkWyo1Q3xrcCPyqt0zhz3h7qKdz3v7dPUy9zEh7cE941F-oU_E4EH6VTSxQaYmuz4Yw2qtcFb6xIiYxP-SjgJyJjyEy5SXWpVemIX_1uCYkra-tltZfWzgAeTowcdtRGHh4MujtdOeTSIui6vhnmV0lsYpk-OYVE4eRx7I3neOur1FxMjmBckZUQ5xMG1ioUiitGnE0U315b4hfrp2hFI1m9vXWJbp3kmCaoJzmPFU9Q7k8MPqYlDz88QA9iq8UUFVEc1ZJL-dsW9j-drdJLxkqqmg2qFM8g75Q__D51yDSUNrLf1xYKIRaZw87QvYgpqGSUI5V0KtI0C1mjn1y2ROTVOB_VA0ZSkSm4RJtV913cDLqyIQfTGG7V75bLu8V7tBFI1G74NVUHFB5y9da0T2Cy302Alnnf1Ii7ZOhk2sAropYmUoDhOH3ThwwTEzqgX-9wdLzmfEIQrQuPB8P-FEbgIX7dt0QmLYmE6BUfPpVXoeVANe7ILDhVGoGW5x6wJ2zf9W-j66HgMOP1eiwiBV9PQL6oUoowgweBN1aiwRRGbRmafLy41NWV7LUiasafXNXz_8NMg7VMvY92YEts2bcH4OP7QmFvrclJARuGCCVsH4b_v4cogvOKw500J4TJVrL2WKDcxZnyIyTj4DzQz0VL-Jc20FeCRLk88ze5KvC7Y83le8YwlLDROa3CtSpDJvQWHqzhBqJnouhU-u_U6BIFa4s_Gh-XvvCc_Nv_g5Vw47QL1OKedssPWY4xkZEzpY9qVKISJlrVlm3CYvL1S0nnwcUEl321re-seXXBCL-89YUHp_7DjOSYi3Hf-yIwkDeAmQ0OlZByH2zFyCj86EmR4iF49oR8qjNi_-Lk16YRXqrVVID2x02b_"}}
{"timestamp":"2025-09-22T13:09:54.897Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Select-String -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -Pattern \\\"shift_violation_\\\"\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_ng7eEPPBu5HhsvU3Ov2RloXI"}}
{"timestamp":"2025-09-22T13:09:54.897Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_ng7eEPPBu5HhsvU3Ov2RloXI","output":"{\"output\":\"\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:248:                var shiftRuleViolationVar = solver.MakeIntVar(0, context.Tasks.Count\\r\\n, $\\\"shift_violation_{personnel.Id}\\\");\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:249:                auxiliaryVars[$\\\"shift_violation_{personnel.Id}\\\"] = shiftRuleViolatio\\r\\nnVar;\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:445:                var violationVarKey = $\\\"batch_aware_shift_violation_{taskId}_{person\\r\\nnelId}\\\";\\r\\nNPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs:1007:            // foreach (var kvp in auxiliaryVars.Where(v => v.Key.StartsWith(\\\"shift\\r\\n_violation_\\\")))\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:09:54.897Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:10:22.994Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Summarizing LinearProgrammingEngine analysis**\n\nI'm preparing a detailed summary of the LinearProgrammingEngine focusing on its workflow, critical issues like disabled constraints and fairness calculation problems, and maintainability concerns due to the large, comment-heavy code. I'll organize findings with clear bullet points referencing specific lines to highlight main risks and provide improvement suggestions as the user requested."}}
{"timestamp":"2025-09-22T13:10:32.406Z","type":"event_msg","payload":{"type":"agent_message","message":"**Overview**\n- `OptimizeAsync` builds an OR-Tools CBC model, wires decision/auxiliary variables, tries to layer three constraint tiers, and maximizes a composite score before post-validating (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:51`).\n- Variable generation already filters by prefilter cache and unavailability indices, while fairness helpers add workload and participation auxiliaries for later constraint/objective use (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:141`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:205`).\n\n**Key Risks**\n- Almost every Level 2/Level 3 and unavailability constraint hook stays commented out, so the solver currently enforces only assignment completeness/basic time clashes; e.g., `AddDynamicShiftRuleConstraints`, `AddDailyWorkloadConstraints`, and `AddFLPriorityConstraints` are never reached (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:393`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:396`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:588`).\n- The fairness constraint `minPersonnelUtilization` hard-codes a minimum of two participants whenever total tasks ≤10, making small or single-staffer scenarios infeasible (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:886`).\n- Multiple average-workload calculations divide by `context.AvailablePersonnel.Count` without guarding against zero, risking runtime failures for empty or filtered personnel pools (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:219`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:847`).\n- Level-1 “time conflict” constraints group by calendar date only, so a person is limited to one task per day regardless of shift/time window, which is far stricter than typical scheduling rules (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:309`).\n- Fairness auxiliaries are unconstrained in the objective—the reward loop is commented out—so even if fairness constraints stay, the model has no incentive to reduce deviations (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:1028`).\n\n**Suggestions**\n- Decide which Level 2/3 constraint modules and penalties are production-ready, re-enable them, and add regression tests that verify each constraint set affects feasibility and the objective.\n- Fix fairness math: handle low-staff scenarios gracefully, keep workload averages finite, and reconnect deviation variables to the objective so they actually steer the solution.\n- Refine time-conflict modeling to respect shift/time granularity (e.g., include `ShiftId`/time spans in the grouping) and add unit tests covering overlapping vs. non-overlapping assignments."}}
{"timestamp":"2025-09-22T13:10:32.418Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":789037,"cached_input_tokens":754688,"output_tokens":9485,"reasoning_output_tokens":6528,"total_tokens":798522},"last_token_usage":{"input_tokens":40979,"cached_input_tokens":40576,"output_tokens":1978,"reasoning_output_tokens":1344,"total_tokens":42957},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:10:32.418Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Summarizing LinearProgrammingEngine analysis**\n\nI'm preparing a detailed summary of the LinearProgrammingEngine focusing on its workflow, critical issues like disabled constraints and fairness calculation problems, and maintainability concerns due to the large, comment-heavy code. I'll organize findings with clear bullet points referencing specific lines to highlight main risks and provide improvement suggestions as the user requested."}],"content":null,"encrypted_content":"[encoded data, 9484 characters]"}}
{"timestamp":"2025-09-22T13:10:32.418Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"**Overview**\n- `OptimizeAsync` builds an OR-Tools CBC model, wires decision/auxiliary variables, tries to layer three constraint tiers, and maximizes a composite score before post-validating (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:51`).\n- Variable generation already filters by prefilter cache and unavailability indices, while fairness helpers add workload and participation auxiliaries for later constraint/objective use (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:141`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:205`).\n\n**Key Risks**\n- Almost every Level 2/Level 3 and unavailability constraint hook stays commented out, so the solver currently enforces only assignment completeness/basic time clashes; e.g., `AddDynamicShiftRuleConstraints`, `AddDailyWorkloadConstraints`, and `AddFLPriorityConstraints` are never reached (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:393`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:396`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:588`).\n- The fairness constraint `minPersonnelUtilization` hard-codes a minimum of two participants whenever total tasks ≤10, making small or single-staffer scenarios infeasible (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:886`).\n- Multiple average-workload calculations divide by `context.AvailablePersonnel.Count` without guarding against zero, risking runtime failures for empty or filtered personnel pools (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:219`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:847`).\n- Level-1 “time conflict” constraints group by calendar date only, so a person is limited to one task per day regardless of shift/time window, which is far stricter than typical scheduling rules (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:309`).\n- Fairness auxiliaries are unconstrained in the objective—the reward loop is commented out—so even if fairness constraints stay, the model has no incentive to reduce deviations (`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:1028`).\n\n**Suggestions**\n- Decide which Level 2/3 constraint modules and penalties are production-ready, re-enable them, and add regression tests that verify each constraint set affects feasibility and the objective.\n- Fix fairness math: handle low-staff scenarios gracefully, keep workload averages finite, and reconnect deviation variables to the objective so they actually steer the solution.\n- Refine time-conflict modeling to respect shift/time granularity (e.g., include `ShiftId`/time spans in the grouping) and add unit tests covering overlapping vs. non-overlapping assignments."}]}}
{"timestamp":"2025-09-22T13:10:48.712Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"回答使用中文"}]}}
{"timestamp":"2025-09-22T13:10:48.713Z","type":"event_msg","payload":{"type":"user_message","message":"回答使用中文","kind":"plain"}}
{"timestamp":"2025-09-22T13:10:48.713Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:10:51.900Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing Chinese summary response**"}}
{"timestamp":"2025-09-22T13:11:08.230Z","type":"event_msg","payload":{"type":"agent_message","message":"**总体概览**\n- `OptimizeAsync` 会用 OR-Tools CBC 求解器搭建模型、创建决策/辅助变量、尝试逐层施加约束并设定目标函数，然后再做解的验证与日志输出（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:51`）。\n- 变量构建阶段已结合预筛选缓存和不可用索引过滤“任务-人员”组合，公平性部分创建的工作量/参与辅助变量后续会参与约束或目标设置（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:141`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:205`）。\n\n**主要风险**\n- Level 2、Level 3 以及人员不可用等关键约束大多仍注释掉，模型当前几乎只保证“任务必须被唯一分配”和“同一天不能安排多个任务”，例如 `AddDynamicShiftRuleConstraints`、`AddDailyWorkloadConstraints`、`AddFLPriorityConstraints` 等未被调用（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:393`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:396`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:588`）。\n- 公平性约束里的 `minPersonnelUtilization` 对小规模任务场景过于死板：当任务数 ≤ 10 时强制至少两个人参与，会直接让小团队或单人场景无解（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:886`）。\n- 多处平均工作量计算直接除以 `context.AvailablePersonnel.Count`，人员列表若为空（或被提前过滤成 0）会触发除零异常（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:219`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:847`）。\n- Level 1 中“时间冲突”约束只按日期分组，未区分班次/时间段；导致同一天同一人只能接一个任务，远比常规排班约束苛刻（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:309`）。\n- 公平性偏差辅助变量未真正纳入目标函数（奖励逻辑被注释），即便添加了约束也无法引导求解器主动平衡负载（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:1028`）。\n\n**改进建议**\n1. 明确哪些 Level 2/3 约束已准备好上线，解除注释并为每类约束补充回归测试，验证它们确实能影响可行性或目标值。\n2. 调整公平性模型：处理人员数量较少时的边界情况，保证平均工作量计算安全，同时重新启用（或重写）偏差奖励，让求解器能主动趋向均衡。\n3. 优化时间冲突判断，将 `ShiftId` 或时间窗纳入分组，区分真正的时间重叠，并为“同日不同班次”与“同班次冲突”编写单元测试。"}}
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{"timestamp":"2025-09-22T13:11:08.360Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Preparing Chinese summary response**"}],"content":null,"encrypted_content":"gAAAAABo0Urc4EzaYFmpMWQmzI6LXS5hg67DjdtstyzxcsBZfW-arJfvudMJ2M6kA0wBp89rcH50lztvYqQG4YjUqc2zUMGw71GQww6pkoiJfbAOGO8cjydF27A1A9Aaa2jo1-hRX6XSazY6ifoY7IfqwISh2HgRMJ2r8fxVpX-azy820mU_ecnMcyugAF1JGIrdlcBYHOIMHbgclbqx8zlNfnGlQBlKgODoH0RGUv26JLNUbQYMfW7mS9_bO8iK0QCZESmss_n35UgeHwzaiknkh4l13uF0q8cZYZvFooZO00d8vBV-laglpGVzCIXhlMq5AmrtrGgUGrO2Igg_33KZrEMMrOtMRofsrM0jy7kQITu1Jfb7yqOIrJht2WmMkl0q5vL5u-EewD_m8oLeiK8OoTBgVT7KQh82KKsHWQHHXsqvUoleHiMUOwoJXjzyqCU9z51yXSt5zfPP4yDLhGRLo180hwo1yDwg_vPNwk-YDwH9UKj5rX36Q4woIw6vS4Zwq9ZwSzVwsuVGU4WzKNFOCZRuR3CNScm6JfK-RpPyEE4asqYHDK_6yzjxziRR4m_v-6WTTK0ra8RM8WvQasb0VqtvU_bNzav7PBXnzPQP-KrSriBgR9A2GvoxUCzh5qRCNFnYPYcA8Nzx43Mh58aGAukRDDJQcpvecvtLo83TXeC-gVnTGbZDEYmOVYFBuh0jGDixOvXY5BI3188L_b_oO67WvOaz9N6ssWjHTX4YZ20lXxQdl8F9aEDImfPxJ_T8QtE5Nd3altKGz0ND1GS-ciRWpvLq0Jq36R0MjljkSE9BluLu_DhnXwYqIyD4qKMZb2i2TYf7iAy9MdbJbzvFCWPUd-GqieuBUuMhDKt7yNSvlcPFw7Dpo0NsM7TfI7ywbfw7NsCpSw6RTDSuu5jsyGBeGBNT7Q=="}}
{"timestamp":"2025-09-22T13:11:08.360Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"**总体概览**\n- `OptimizeAsync` 会用 OR-Tools CBC 求解器搭建模型、创建决策/辅助变量、尝试逐层施加约束并设定目标函数，然后再做解的验证与日志输出（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:51`）。\n- 变量构建阶段已结合预筛选缓存和不可用索引过滤“任务-人员”组合，公平性部分创建的工作量/参与辅助变量后续会参与约束或目标设置（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:141`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:205`）。\n\n**主要风险**\n- Level 2、Level 3 以及人员不可用等关键约束大多仍注释掉，模型当前几乎只保证“任务必须被唯一分配”和“同一天不能安排多个任务”，例如 `AddDynamicShiftRuleConstraints`、`AddDailyWorkloadConstraints`、`AddFLPriorityConstraints` 等未被调用（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:393`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:396`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:588`）。\n- 公平性约束里的 `minPersonnelUtilization` 对小规模任务场景过于死板：当任务数 ≤ 10 时强制至少两个人参与，会直接让小团队或单人场景无解（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:886`）。\n- 多处平均工作量计算直接除以 `context.AvailablePersonnel.Count`，人员列表若为空（或被提前过滤成 0）会触发除零异常（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:219`, `NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:847`）。\n- Level 1 中“时间冲突”约束只按日期分组，未区分班次/时间段；导致同一天同一人只能接一个任务，远比常规排班约束苛刻（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:309`）。\n- 公平性偏差辅助变量未真正纳入目标函数（奖励逻辑被注释），即便添加了约束也无法引导求解器主动平衡负载（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:1028`）。\n\n**改进建议**\n1. 明确哪些 Level 2/3 约束已准备好上线，解除注释并为每类约束补充回归测试，验证它们确实能影响可行性或目标值。\n2. 调整公平性模型：处理人员数量较少时的边界情况，保证平均工作量计算安全，同时重新启用（或重写）偏差奖励，让求解器能主动趋向均衡。\n3. 优化时间冲突判断，将 `ShiftId` 或时间窗纳入分组，区分真正的时间重叠，并为“同日不同班次”与“同班次冲突”编写单元测试。"}]}}
{"timestamp":"2025-09-22T13:11:37.917Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"分析整个方法"}]}}
{"timestamp":"2025-09-22T13:11:37.917Z","type":"event_msg","payload":{"type":"user_message","message":"分析整个方法","kind":"plain"}}
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{"timestamp":"2025-09-22T13:11:44.775Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing comprehensive method analysis**"}}
{"timestamp":"2025-09-22T13:12:06.017Z","type":"event_msg","payload":{"type":"agent_message","message":"**整体流程**\n- `OptimizeAsync` 负责完整流水线：记录上下文规模 → 调 `CreateAdvancedSolver` 配置 CBC 求解器 → 生成决策变量/辅助变量 → 分批调用 Level1/2/3、公平性与目标函数构建 → 求解并调用 `ProcessEnhancedSolverResult` 解析输出；异常时回退 `CreateFailureSolution`（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:51`）。\n\n**核心方法解析**\n- `CreateAdvancedSolver`：建立 CBC 求解器，设置 10 分钟时限、启发式/割平面/预处理等参数，线程数设 0 代表使用全部内核（`LinearProgrammingEngine.cs:112`）。\n- `BuildDecisionVariables`：遍历任务×人员组合，先用 `IsPersonnelUnavailableForTask` 防止创建不可用变量，再参考 `PrefilterResults` 判断是否生成布尔变量，统计创建/过滤数量用于日志（`LinearProgrammingEngine.cs:141`）。\n- `BuildAuxiliaryVariables`：构建工作量整型变量、偏差浮点变量、按日期的冲突指示变量；班次违规及 FL 违规辅助变量暂被整段注释（`LinearProgrammingEngine.cs:205`）。\n- `AddLevel1BasicConstraints`：落实“每个任务恰好分配一次”、“同一人员同一天最多一个任务”，并将冲突指示变量与决策变量关联；还把个人任务数绑定到 `workload_*` 辅助变量（`LinearProgrammingEngine.cs:278`）。\n- `AddLevel2CombinationConstraints`：当前主体只输出日志，真正的批次规则、日工作量约束函数保留在下方却未被调用（`LinearProgrammingEngine.cs:382`）。\n- `AddDynamicShiftRuleConstraints`：批次验证重度实现，构建 `PersonnelTaskMatrix`、调用 `_shiftRuleEngine` 进行批量校验，依验证结果生成违规布尔变量和多种约束，还包含全局时间窗口/批次完整性约束，但由于上层注释永远不会执行（`LinearProgrammingEngine.cs:407`）。\n- `AddDailyWorkloadConstraints`：限制单人单日工作时长上限（默认 8h），同样未被启用（`LinearProgrammingEngine.cs:531`）。\n- `AddLevel3BusinessConstraints`：占位方法仅写入日志，内部各细分约束全部注释，包括 FL 优先、技能匹配、项目连续性、资质、不可用性硬约束（`LinearProgrammingEngine.cs:576`）。\n- `AddFairnessConstraints`：利用辅助变量强制“工作量=平均值±偏差”，设置最大工作量、最小参与人数等；参与人数至少为 2（小任务）或 40%（大任务），且偏差变量未进入目标函数，导致刚性约束易致无解（`LinearProgrammingEngine.cs:835`）。\n- `SetEnhancedObjectiveFunction`：最大化目标，仍然调用基础得分、约束惩罚（只针对冲突变量），公平性奖励、业务奖励逻辑被注释；基础得分会放大预筛选评分并叠加技能奖励（`LinearProgrammingEngine.cs:932`）。\n- `ProcessEnhancedSolverResult`：读取解中取值 > 0.5 的变量生成分配结果，重新计算评分（不使用 OR-Tools 里的目标值），并调用 `ValidateSolutionQuality` 估算任务完整率/冲突率/负载均衡等；验证阶段也未实际连接所有辅助变量（`LinearProgrammingEngine.cs:1087`）。\n- `CheckQualificationCompatibilityAsync`、`IsPersonnelUnavailableForTask` 等辅助方法提供兜底逻辑，和批量验证一起确保数据一致性（`LinearProgrammingEngine.cs:1543`, `LinearProgrammingEngine.cs:2259`）。\n\n**突出问题**\n- Level 2/3 绝大部分约束尚未启用，当前模型只依靠 Level 1、部分公平性约束与目标函数评分，难以满足设计宣称的“完整业务规则”。\n- 时间冲突按天聚合，忽略班次/时段，导致同日排班过度保守（`LinearProgrammingEngine.cs:309`）。\n- 公平性约束强制最小参与人数与平均负载，未考虑任务/人员稀少场景，且奖励函数没启用使得偏差变量仅作为僵硬约束。\n- 多处平均值计算未防御“AvailablePersonnel=0”，可能抛出除零异常。\n- 目标函数中约束惩罚仅覆盖 `conflict_*`，其它违规变量即便未来启用约束也不会影响目标优化。\n\n**建议**\n1. 明确需要启用的 Level 2/3 子约束，解除注释并配合 `_shiftRuleEngine` 增加集成测试，验证各类规则的影响。\n2. 重构时间冲突逻辑，改为区分 `ShiftId` 或具体时间窗；为“同日不同班次”场景补充测试。\n3. 调整公平性约束策略（允许小团队、保证除法安全），同时恢复偏差奖励或改用软约束形式，避免直接导致无解。\n4. 若保留违规辅助变量，应在 `AddConstraintViolationPenalties` 中加入对应惩罚项，使其与目标函数产生反馈。\n5. 补充异常/空集防护，确保在无可用人员或任务时能快速返回可读错误。"}}
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{"timestamp":"2025-09-22T13:12:06.022Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"**整体流程**\n- `OptimizeAsync` 负责完整流水线：记录上下文规模 → 调 `CreateAdvancedSolver` 配置 CBC 求解器 → 生成决策变量/辅助变量 → 分批调用 Level1/2/3、公平性与目标函数构建 → 求解并调用 `ProcessEnhancedSolverResult` 解析输出；异常时回退 `CreateFailureSolution`（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:51`）。\n\n**核心方法解析**\n- `CreateAdvancedSolver`：建立 CBC 求解器，设置 10 分钟时限、启发式/割平面/预处理等参数，线程数设 0 代表使用全部内核（`LinearProgrammingEngine.cs:112`）。\n- `BuildDecisionVariables`：遍历任务×人员组合，先用 `IsPersonnelUnavailableForTask` 防止创建不可用变量，再参考 `PrefilterResults` 判断是否生成布尔变量，统计创建/过滤数量用于日志（`LinearProgrammingEngine.cs:141`）。\n- `BuildAuxiliaryVariables`：构建工作量整型变量、偏差浮点变量、按日期的冲突指示变量；班次违规及 FL 违规辅助变量暂被整段注释（`LinearProgrammingEngine.cs:205`）。\n- `AddLevel1BasicConstraints`：落实“每个任务恰好分配一次”、“同一人员同一天最多一个任务”，并将冲突指示变量与决策变量关联；还把个人任务数绑定到 `workload_*` 辅助变量（`LinearProgrammingEngine.cs:278`）。\n- `AddLevel2CombinationConstraints`：当前主体只输出日志，真正的批次规则、日工作量约束函数保留在下方却未被调用（`LinearProgrammingEngine.cs:382`）。\n- `AddDynamicShiftRuleConstraints`：批次验证重度实现，构建 `PersonnelTaskMatrix`、调用 `_shiftRuleEngine` 进行批量校验，依验证结果生成违规布尔变量和多种约束，还包含全局时间窗口/批次完整性约束，但由于上层注释永远不会执行（`LinearProgrammingEngine.cs:407`）。\n- `AddDailyWorkloadConstraints`：限制单人单日工作时长上限（默认 8h），同样未被启用（`LinearProgrammingEngine.cs:531`）。\n- `AddLevel3BusinessConstraints`：占位方法仅写入日志，内部各细分约束全部注释，包括 FL 优先、技能匹配、项目连续性、资质、不可用性硬约束（`LinearProgrammingEngine.cs:576`）。\n- `AddFairnessConstraints`：利用辅助变量强制“工作量=平均值±偏差”，设置最大工作量、最小参与人数等；参与人数至少为 2（小任务）或 40%（大任务），且偏差变量未进入目标函数，导致刚性约束易致无解（`LinearProgrammingEngine.cs:835`）。\n- `SetEnhancedObjectiveFunction`：最大化目标，仍然调用基础得分、约束惩罚（只针对冲突变量），公平性奖励、业务奖励逻辑被注释；基础得分会放大预筛选评分并叠加技能奖励（`LinearProgrammingEngine.cs:932`）。\n- `ProcessEnhancedSolverResult`：读取解中取值 > 0.5 的变量生成分配结果，重新计算评分（不使用 OR-Tools 里的目标值），并调用 `ValidateSolutionQuality` 估算任务完整率/冲突率/负载均衡等；验证阶段也未实际连接所有辅助变量（`LinearProgrammingEngine.cs:1087`）。\n- `CheckQualificationCompatibilityAsync`、`IsPersonnelUnavailableForTask` 等辅助方法提供兜底逻辑，和批量验证一起确保数据一致性（`LinearProgrammingEngine.cs:1543`, `LinearProgrammingEngine.cs:2259`）。\n\n**突出问题**\n- Level 2/3 绝大部分约束尚未启用，当前模型只依靠 Level 1、部分公平性约束与目标函数评分，难以满足设计宣称的“完整业务规则”。\n- 时间冲突按天聚合，忽略班次/时段，导致同日排班过度保守（`LinearProgrammingEngine.cs:309`）。\n- 公平性约束强制最小参与人数与平均负载，未考虑任务/人员稀少场景，且奖励函数没启用使得偏差变量仅作为僵硬约束。\n- 多处平均值计算未防御“AvailablePersonnel=0”，可能抛出除零异常。\n- 目标函数中约束惩罚仅覆盖 `conflict_*`，其它违规变量即便未来启用约束也不会影响目标优化。\n\n**建议**\n1. 明确需要启用的 Level 2/3 子约束，解除注释并配合 `_shiftRuleEngine` 增加集成测试，验证各类规则的影响。\n2. 重构时间冲突逻辑，改为区分 `ShiftId` 或具体时间窗；为“同日不同班次”场景补充测试。\n3. 调整公平性约束策略（允许小团队、保证除法安全），同时恢复偏差奖励或改用软约束形式，避免直接导致无解。\n4. 若保留违规辅助变量，应在 `AddConstraintViolationPenalties` 中加入对应惩罚项，使其与目标函数产生反馈。\n5. 补充异常/空集防护，确保在无可用人员或任务时能快速返回可读错误。"}]}}
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{"timestamp":"2025-09-22T13:15:50.308Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Reviewing CreateAdvancedSolver parameter usage**\n\nI'll carefully analyze the CreateAdvancedSolver method focusing on potential issues with the solver parameter string format—spaces instead of newlines might cause misconfiguration—as well as the unsupported \"threads=0\" value for CBC that likely disables multithreading, and also note unused constants and minor risks around solver setup. I'll reference specific file lines to clearly document these observations."}}
{"timestamp":"2025-09-22T13:15:56.809Z","type":"event_msg","payload":{"type":"agent_message","message":"**CreateAdvancedSolver 评估**  \n- 方法能正确检测 `Solver.CreateSolver` 返回 `null` 并抛出异常，这点没问题（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:112`）。  \n- 传给 `SetSolverSpecificParametersAsString` 的字符串用空格分隔 `\"cuts=1 heuristics=1 presolve=1 threads=0\"`；CBC 通常要求换行或分号分隔，每行一个参数，当前写法大概率只会识别第一个 `cuts=1`（其余被视作同一字符串尾部而忽略），导致启发式、预处理、线程设置可能没生效（`LinearProgrammingEngine.cs:125`）。  \n- CBC 的 `threads` 参数需要正整数，设置 `0` 并不会“使用全部核心”，反而会回落到默认单线程；若想多线程求解需要显式给出核心数，例如 `threads=Environment.ProcessorCount`（`LinearProgrammingEngine.cs:125`）。  \n- 常量 `OPTIMALITY_TOLERANCE`、`MAX_CONSTRAINT_COUNT` 在此方法及全类未被使用，若设计上需要控制 GAP 或约束数量，应补充设置；否则可以删去以免误导（`LinearProgrammingEngine.cs:28`, `LinearProgrammingEngine.cs:30`）。\n\n**建议**  \n1. 将参数字符串改为按行写法，并为 `threads` 指定至少 1 的实际核心数：  \n   ```csharp\n   solver.SetSolverSpecificParametersAsString(\n       $\"cuts=1{Environment.NewLine}\" +\n       $\"heuristics=1{Environment.NewLine}\" +\n       $\"presolve=1{Environment.NewLine}\" +\n       $\"threads={Math.Max(1, Environment.ProcessorCount)}\");\n   ```  \n2. 若确实需要控制 CBC 最优容忍度或约束数量，可利用 `cbc` 参数如 `allowable_gaps`、`max_num_cuts`，或在建模阶段主动检查约束数，避免“配置常量却未使用”。"}}
{"timestamp":"2025-09-22T13:15:56.809Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":898352,"cached_input_tokens":828800,"output_tokens":13110,"reasoning_output_tokens":7680,"total_tokens":911462},"last_token_usage":{"input_tokens":37534,"cached_input_tokens":36224,"output_tokens":1360,"reasoning_output_tokens":896,"total_tokens":38894},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:15:56.810Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Reviewing CreateAdvancedSolver parameter usage**\n\nI'll carefully analyze the CreateAdvancedSolver method focusing on potential issues with the solver parameter string format—spaces instead of newlines might cause misconfiguration—as well as the unsupported \"threads=0\" value for CBC that likely disables multithreading, and also note unused constants and minor risks around solver setup. I'll reference specific file lines to clearly document these observations."}],"content":null,"encrypted_content":"[encoded data, 6328 characters]"}}
{"timestamp":"2025-09-22T13:15:56.810Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"**CreateAdvancedSolver 评估**  \n- 方法能正确检测 `Solver.CreateSolver` 返回 `null` 并抛出异常，这点没问题（`NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs:112`）。  \n- 传给 `SetSolverSpecificParametersAsString` 的字符串用空格分隔 `\"cuts=1 heuristics=1 presolve=1 threads=0\"`；CBC 通常要求换行或分号分隔，每行一个参数，当前写法大概率只会识别第一个 `cuts=1`（其余被视作同一字符串尾部而忽略），导致启发式、预处理、线程设置可能没生效（`LinearProgrammingEngine.cs:125`）。  \n- CBC 的 `threads` 参数需要正整数，设置 `0` 并不会“使用全部核心”，反而会回落到默认单线程；若想多线程求解需要显式给出核心数，例如 `threads=Environment.ProcessorCount`（`LinearProgrammingEngine.cs:125`）。  \n- 常量 `OPTIMALITY_TOLERANCE`、`MAX_CONSTRAINT_COUNT` 在此方法及全类未被使用，若设计上需要控制 GAP 或约束数量，应补充设置；否则可以删去以免误导（`LinearProgrammingEngine.cs:28`, `LinearProgrammingEngine.cs:30`）。\n\n**建议**  \n1. 将参数字符串改为按行写法，并为 `threads` 指定至少 1 的实际核心数：  \n   ```csharp\n   solver.SetSolverSpecificParametersAsString(\n       $\"cuts=1{Environment.NewLine}\" +\n       $\"heuristics=1{Environment.NewLine}\" +\n       $\"presolve=1{Environment.NewLine}\" +\n       $\"threads={Math.Max(1, Environment.ProcessorCount)}\");\n   ```  \n2. 若确实需要控制 CBC 最优容忍度或约束数量，可利用 `cbc` 参数如 `allowable_gaps`、`max_num_cuts`，或在建模阶段主动检查约束数，避免“配置常量却未使用”。"}]}}
{"timestamp":"2025-09-22T13:17:29.831Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"帮我修改"}]}}
{"timestamp":"2025-09-22T13:17:29.832Z","type":"event_msg","payload":{"type":"user_message","message":"帮我修改","kind":"plain"}}
{"timestamp":"2025-09-22T13:17:29.832Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:17:52.667Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing method parameter modifications**"}}
{"timestamp":"2025-09-22T13:17:56.730Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":936357,"cached_input_tokens":866304,"output_tokens":14397,"reasoning_output_tokens":8576,"total_tokens":950754},"last_token_usage":{"input_tokens":38005,"cached_input_tokens":37504,"output_tokens":1287,"reasoning_output_tokens":896,"total_tokens":39292},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:17:56.731Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Preparing method parameter modifications**"}],"content":null,"encrypted_content":"[encoded data, 6372 characters]"}}
{"timestamp":"2025-09-22T13:17:56.731Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"python - <<'PY'\\nfrom pathlib import Path\\npath = Path(r'NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs')\\ntext = path.read_text(encoding='utf-8')\\nold = \\\"\\\"\\\"            solver.SetSolverSpecificParametersAsString(\\\\r\\\\n                \\\\\\\"cuts=1 \\\\\\\" +           // 启用割平面\\\\r\\\\n                \\\\\\\"heuristics=1 \\\\\\\" +     // 启用启发式算法\\\\r\\\\n                \\\\\\\"presolve=1 \\\\\\\" +       // 启用预处理\\\\r\\\\n                \\\\\\\"threads=0\\\\\\\"           // 使用所有可用线程\\\\r\\\\n            );\\\\r\\\\n\\\"\\\"\\\"\\nnew = \\\"\\\"\\\"            var solverParameters = string.Join(Environment.NewLine, new[]\\\\r\\\\n            {\\\\r\\\\n                \\\\\\\"cuts=1\\\\\\\",            // 启用割平面\\\\r\\\\n                \\\\\\\"heuristics=1\\\\\\\",      // 启用启发式算法\\\\r\\\\n                \\\\\\\"presolve=1\\\\\\\",        // 启用预处理\\\\r\\\\n                $\\\\\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\\\\\" // 线程数量取实际核心数\\\\r\\\\n            });\\\\r\\\\n            solver.SetSolverSpecificParametersAsString(solverParameters);\\\\r\\\\n\\\"\\\"\\\"\\nif old not in text:\\n    raise SystemExit('target snippet not found')\\npath.write_text(text.replace(old, new), encoding='utf-8')\\nPY\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_X6BeJJ9i7HaX0QJbCzQvFc9L"}}
{"timestamp":"2025-09-22T13:17:56.731Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_X6BeJJ9i7HaX0QJbCzQvFc9L","output":"{\"output\":\"����λ�� ��:1 �ַ�: 11\\r\\n+ python - <<'PY'\\r\\n+           ~\\r\\n�ض������������ȱ���ļ��淶��\\r\\n����λ�� ��:1 �ַ�: 10\\r\\n+ python - <<'PY'\\r\\n+          ~\\r\\n��<���������Ϊ����ʹ�ö������ġ�\\r\\n����λ�� ��:1 �ַ�: 11\\r\\n+ python - <<'PY'\\r\\n+           ~\\r\\n��<���������Ϊ����ʹ�ö������ġ�\\r\\n����λ�� ��:2 �ַ�: 1\\r\\n+ from pathlib import Path\\r\\n+ ~~~~\\r\\n�����԰汾�в�֧�֡�from���ؼ��֡�\\r\\n����λ�� ��:6 �ַ�: 298\\r\\n+ ... \\\",        // ����Ԥ����\\\\r\\\\n                $\\\\\\\"threads={Math.Max(1, Environ ...\\r\\n+                                                                  ~\\r\\n��,������ȱ�ٱ���ʽ��\\r\\n����λ�� ��:6 �ַ�: 299\\r\\n+ ...            $\\\\\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\\\\\" //  ...\\r\\n+                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n����ʽ������а�������ı�ǡ�Environment.ProcessorCount����\\r\\n����λ�� ��:6 �ַ�: 298\\r\\n+ ... \\\",        // ����Ԥ����\\\\r\\\\n                $\\\\\\\"threads={Math.Max(1, Environ ...\\r\\n+                                                                  ~\\r\\n����ʽ��ȱ���ҡ�)����\\r\\n����λ�� ��:6 �ַ�: 286\\r\\n+ ... presolve=1\\\\\\\",        // ����Ԥ����\\\\r\\\\n                $\\\\\\\"threads={Math.Max ...\\r\\n+                                                                 ~\\r\\n��������Ͷ�����ȱ���ҡ�}����\\r\\n����λ�� ��:6 �ַ�: 325\\r\\n+ ...           $\\\\\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\\\\\" // �� ...\\r\\n+                                                                 ~\\r\\n����ʽ������а�������ı�ǡ�)����\\r\\n����λ�� ��:6 �ַ�: 326\\r\\n+ ...          $\\\\\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\\\\\" // �߳� ...\\r\\n+                                                                 ~\\r\\n����ʽ������а�������ı�ǡ�}����\\r\\n��δ�������з����������������Ĵ������ԡ�\\r\\n    + CategoryInfo          : ParserError: (:) [], ParentContainsErrorRecordException\\r\\n    + FullyQualifiedErrorId : MissingFileSpecification\\r\\n \\r\\n\",\"metadata\":{\"exit_code\":1,\"duration_seconds\":0.6}}"}}
{"timestamp":"2025-09-22T13:17:56.731Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:18:07.778Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Addressing PowerShell quoting limitations**"}}
{"timestamp":"2025-09-22T13:18:16.234Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":976324,"cached_input_tokens":905216,"output_tokens":15130,"reasoning_output_tokens":8960,"total_tokens":991454},"last_token_usage":{"input_tokens":39967,"cached_input_tokens":38912,"output_tokens":733,"reasoning_output_tokens":384,"total_tokens":40700},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:18:16.234Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Addressing PowerShell quoting limitations**"}],"content":null,"encrypted_content":"gAAAAABo0UyQgRCaXFAYHoxVcsiBBn7hMJdSWVist8vArN7Pw6sSolYvd17KyRYPqYq801Nk6XQlNCJKumEShfGpGA3LisnXBUEcs4cg_-yznQkoccygC1Jbd3HLbrMKbGxc_ddNWzoYtop9D51HQhooWrJdobQVfJLEkxS5xMMYd9izf_16yYjltMglcarT9N9cCJhl1xJVxBVK7ECacK31-0zyAR14PFnvM6zEvK3udb3Pj0prkQGsbYOI-jtjgiu2NMRFZnhfJjTm2DGRvJ9lvpsQEt61zz6t9x9bb_1faWL0h1rNT_4fz8YN6harFgY5lgKxoqaAiYqJJS-lWPwl4pNxjU9epZiDtuWyQB9Zi9EDT26hedtRUBX8px-GPGOCaghLu2IynEq18DpnXPWHJs6bk9iHXnI0Opx8n1Emjw41gOiRVHbKsHSJuj535irHP8DZiVvcmlDXG4vY9BUQhycC05KognfvtVW_RpegAzCilMk1k9BVHnC6lL2jJQagVwzk2K0iZPlMVGZWKR35LKF-u5Lz45OXQUB-Tun5vKX-q6Kkil2758ZUFMunVumU_ZYNBBNQuCtJsyxSQz6VXJPbo4i7gxDm5R3Sp-IXOc9qc3cB7JjHt1PR3efuOMro-6XrW1L7_FfTXEbdV2-PHI1NA757NNIJiFl_V8sr92aw8h5BHPJf5rCXZCIT88_n6AYe9Mjt5nu_1rpa1AcwpvfnBwfpBpUgBz73OBj27C5e0i5ShFAq7PVFgP34NAqnn2YkGQWbT-Me8Fm5h0cb-OdYXgX1Io2alcfPBj_o9FGJxcIkJHiTWgl49vOUQCWt3tSb_4mbliBXOQDlM4XIbh8ry0b-Yw4X5QWL0akQ47YNMC0_zdEtXWUoa7P8hTdaDHoO8woE5o4u6xoLmIYzi4-DAqDGogBG4Wd9lNByvxkPD_QqN7DbNwnrl6az-oorNysRLW8UuuC0kv5uY3VDf1XMR8r9zglS8bG1RR9B76GuDX4cv-OvMhuYHqK9p6ynh1KjrkKxyFkqVleRTK-O3CxgvtrmOYEFcAavE6gfRl_kxbJdE8fI2fx83Ccb_FpXnNJiG-nlkXQJkpI1sdTFeVroY6BSivkDOx3zrhlMYyb63J91lDkunpx6J9Ady81Fwqxv88KGy3McYZr6u4Gkr3kCmRVJzSbWrL7kpd2lF9h7mxhr_Yn2e4MsVy4ZJeKz1pYA3zEdHJdiq-Gi88-06_XE3cNlgvLV8LHNkpkyXr_wNV_chaPOM_fzbVTBVJNSdTetmN7xNlqhl540rfALrDy1jmQkpxzWFOI_WN0D-YhCV6qcfaJCZK1TAbtysiYmHQzYN-QyxIKbx_zKTZgE-w3AQG2UNLQmlwlAhGS3BGXF6xG9PyoPVB8T-6aHeugR1RCYrhQwA1hqvaFcCR8EvqNW91N5s3-O8hst5PQIFpGuV8DeosB47gSf0XA6tEZc7EN_TOGcRfWhsa9oYFsohsxElPCiMvKAccWToLgk8qoXE3GAyuUkE9VRVnjv7XwcAv13ghQx0u-AG6-hAcwu_cS7iyBFwkPAa4DqIgrRV_w9bXo1VU1-8BxjQkL6OsXv6s_ZSSq2dfSzvS0Co2zbCjOSwcEi8kAXE_v93dvZFcLVK0_m7rHy--wW0eSzgmg1CuMkaCpXIcP8wN3a0YnK_uSlw9-rMDqh9kbXjzx-3QlhhF-OSU7v6N_tdNWzTvR8_EV6jTh0SickphLyvprY56Xkd7t8PMW9bzGPDBlvKAOfkBP1kZhOToRQ6bDNCF-ruKk27e3GZk4jHfExOHZPuiV-PhHSAEtcKhnSSanMGbBV8cJoCWLIpmlvaZxBWH_8DweTSM73e37-VXGgKcCSuu95Sj_vjlJV84fux9oNjzwYV4NAmV18_IoGdcDowhkTlBXfmfGhAjoZ6fIJeTXUIxDcD8Y6ONsGo8qd3l9OeT3V6HcvNyOZ-bgM1BlluIXsQInWtBEV2mu4tkfoZH8AwO3XvbGRlkZFOuCQC6r_aFjfg_bVW1m6iBuWWXdBoz9HnR2i7qUFq6DA38sv32XPE55I8IAJRMEW12JJwLOhk6lsz9SGFg_-eaDCnZpepQpi7EkEboyL9EjF4Tg9cUkRUuAVkTxpGkIn4C3ynCo3mmsYjh-d_fo91mahkfgxuv-3PTaFcrZhGP8yR8X0VJZmtfu5wtjI3OXEQ09GqsOqUeadvFTE1b3d9OZjYgIM5WcXBpCjyY65M8mOz8RCnvNVpI-JrDNi7ddCYoXzClfCtwpe6PlvhpFF0oHuE4CJmMLpg4pnizA6wntuBZoshw4mNF449ltfT87WbVhYjgOHej2_YxUPzXJ-HITupt7WkrHVdlq3fhz8tZke-wRJiG2lAWK_3cvJdf92yYuVINBdLzyq5WGXSvhOHhDDnopfd4KFiPuYmJRdkeHBAjkYLUj09xyFYTKfXhQY2VLTaYrr0AYhAv-4tZqFWuRB1GG5cVK0uGF-lT2GojujnhW0XntHAsZaTSzDAajdzVSTtuyRnMrGv6RaOdhgCc1mJQmo4tNrnBPLCjcSwLlGDMrPqxbXgS-M79SFQvtiaz0d0abN2-eiJS3mRSERhVt7RfEdNDQOkImwxb_fHQn0qJIMidCRr0WspgPQMkVnd29I5iRUNt38lx39gI5Aw1UM7XayG2_pJ07aiqDtmtAYhC4Uq-THsspfAzZsTw=="}}
{"timestamp":"2025-09-22T13:18:16.234Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$text = Get-Content -Path $path -Raw;\\n$old = @\\\"\\n            solver.SetSolverSpecificParametersAsString(\\r\\n                \\\"cuts=1 \\\" +           // 启用割平面\\r\\n                \\\"heuristics=1 \\\" +     // 启用启发式算法\\r\\n                \\\"presolve=1 \\\" +       // 启用预处理\\r\\n                \\\"threads=0\\\"           // 使用所有可用线程\\r\\n            );\\r\\n\\\"@;\\n$new = @\\\"\\n            var solverParameters = string.Join(Environment.NewLine, new[]\\r\\n            {\\r\\n                \\\"cuts=1\\\",            // 启用割平面\\r\\n                \\\"heuristics=1\\\",      // 启用启发式算法\\r\\n                \\\"presolve=1\\\",        // 启用预处理\\r\\n                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数\\r\\n            });\\r\\n            solver.SetSolverSpecificParametersAsString(solverParameters);\\r\\n\\\"@;\\nif (-not $text.Contains($old)) { throw 'target snippet not found'; }\\n$text = $text.Replace($old, $new);\\nSet-Content -Path $path -Value $text -Encoding UTF8;\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_VJoLMVgpN1mlg8aW3iPGfv2x"}}
{"timestamp":"2025-09-22T13:18:16.234Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_VJoLMVgpN1mlg8aW3iPGfv2x","output":"{\"output\":\"target snippet not found\\r\\n����λ�� ��:21 �ַ�: 34\\r\\n+ if (-not $text.Contains($old)) { throw 'target snippet not found'; }\\r\\n+                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n    + CategoryInfo          : OperationStopped: (target snippet not found:String) [], RuntimeException\\r\\n    + FullyQualifiedErrorId : target snippet not found\\r\\n \\r\\n\",\"metadata\":{\"exit_code\":1,\"duration_seconds\":0.5}}"}}
{"timestamp":"2025-09-22T13:18:16.234Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:18:19.898Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Checking line indentation style**"}}
{"timestamp":"2025-09-22T13:18:21.097Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1017142,"cached_input_tokens":945536,"output_tokens":15218,"reasoning_output_tokens":8960,"total_tokens":1032360},"last_token_usage":{"input_tokens":40818,"cached_input_tokens":40320,"output_tokens":88,"reasoning_output_tokens":0,"total_tokens":40906},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:18:21.097Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Checking line indentation style**"}],"content":null,"encrypted_content":"gAAAAABo0Uycuahz95tb-JcTK-mSmx5NdBWd7SW3N71bKPGAsi-7OAMl4K2ilWFTtRzziwW9IqphTC0hOAinGg97bTdYO-vtTpNNu9E__-mACn-bz0vZtWGvpJS0-n888HzqjXvVQgnEC32sf1DIuBOceudStG18ln_mbM_YfMjNzUZqZCeGCORxCawTaltbnpMfOwciSoSV9RtNKvtgyT8k4QVpaiZYM6yLeeHZ7tUW_lN49UgQ8CPKRXEybwaG3JBGED6PhuJmolULIgmQnTRlBH4s8jtMMOqOgr3WA2s5mesbc4qfZM24gT6qaoQ0YXS2oXxxdDN3n0hTGspMxp9olv9Mu6KZIMYGTQjxAyyupveaiajTU33ucZ8ckI-zBT8B2Gz8B1hYxBq87wScBmklTAPCt1KcFwsepbqBPzfG8tZzHJh5cW4wwR7RS_-4HxtcwWUPVl2QfydZJQ603Wjislk5ypCWTIlpkVxRTtnPwxjdPS9KTqX8PYURn2hr8VMRReKTIBsYSuM-LFPiajUQFiU57Z5NEQs91tYjxKmozp6R7yMLGFjN8aS3p94PrPhrp6FGe8IrJ4bCFzFKGrqChkRaTlTcnyODzRXN70X0UXCQfAAEU7k4NljMW3Th-kBKj8auiZ65GAcbBbPmI64RJT1_0VCtK3ltqzpvMOznqwSU89248ZrnlHaQg06CxEhiCLSWHVX3hxRLD75a9qe8edSVeDzVmFOy22Bc1Zm-j_I1DEi0M_pcFP4M4gwFACTwr6Wp5MyAvy5OfraofK2lwflKpm7kYNzG6CUz26XckhFeFLdBxVJ2lq1Mx35yWvtxW3QbidVhkamfKWnk_7SvdSZRNQORLaqPbD8ID00cpAQOp7V7o4-nVEx8LfF0e0XR4-CepN6JSVmLB6blH5Zr1q5EcG0FJVcFabRvGfqjYbETIvw2uZcVHuxkzdy7TucDsgSksXJsP6aYF5E8c7RU8pXlQ5YXG1x4cAFMoQ7A79iE7GeDr_k="}}
{"timestamp":"2025-09-22T13:18:21.097Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-String -Pattern \\\"SetSolverSpecificParametersAsString\\\" -Context 0,5\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_f8nowVWm70NyfDDhqol51y9Z"}}
{"timestamp":"2025-09-22T13:18:21.097Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_f8nowVWm70NyfDDhqol51y9Z","output":"{\"output\":\"\\r\\n>             solver.SetSolverSpecificParametersAsString(\\r\\n                  \\\"cuts=1 \\\" +           // 启用割平�?\\r\\n                  \\\"heuristics=1 \\\" +     // 启用启发式算�?\\r\\n                  \\\"presolve=1 \\\" +       // 启用预处�?\\r\\n                  \\\"threads=0\\\"           // 使用所有可用线�?\\r\\n              );\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.4}}"}}
{"timestamp":"2025-09-22T13:18:21.097Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:18:46.043Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Planning precise block replacement**\n\nI’m working out how to reliably find and replace the exact multi-line block calling `solver.SetSolverSpecificParametersAsString` in a PowerShell script while handling tricky indentation and line endings; I plan to read the file as lines, identify the block bounds by searching for the starting line and the closing `);`, then remove and insert the new lines with correct spacing before saving the updated file."}}
{"timestamp":"2025-09-22T13:18:54.681Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1058195,"cached_input_tokens":986368,"output_tokens":17025,"reasoning_output_tokens":10304,"total_tokens":1075220},"last_token_usage":{"input_tokens":41053,"cached_input_tokens":40832,"output_tokens":1807,"reasoning_output_tokens":1344,"total_tokens":42860},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:18:54.682Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Planning precise block replacement**\n\nI’m working out how to reliably find and replace the exact multi-line block calling `solver.SetSolverSpecificParametersAsString` in a PowerShell script while handling tricky indentation and line endings; I plan to read the file as lines, identify the block bounds by searching for the starting line and the closing `);`, then remove and insert the new lines with correct spacing before saving the updated file."}],"content":null,"encrypted_content":"[encoded data, 8376 characters]"}}
{"timestamp":"2025-09-22T13:18:54.682Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$lines = New-Object System.Collections.Generic.List[string];\\n$lines.AddRange((Get-Content -Path $path));\\n$start = -1;\\nfor ($i = 0; $i -lt $lines.Count; $i++) {\\n    if ($lines[$i].Contains('solver.SetSolverSpecificParametersAsString')) {\\n        $start = $i;\\n        break;\\n    }\\n}\\nif ($start -eq -1) { throw 'target start not found'; }\\n$end = $start;\\ndo {\\n    $end++;\\n    if ($end -ge $lines.Count) { throw 'closing line not found'; }\\n} while ($lines[$end].Trim() -ne ');');\\nfor ($j = $end; $j -ge $start; $j--) {\\n    $lines.RemoveAt($j);\\n}\\n$newLines = @(\\n    '            var solverParameters = string.Join(Environment.NewLine, new[]',\\n    '            {',\\n    '                \\\"cuts=1\\\",            // 启用割平面',\\n    '                \\\"heuristics=1\\\",      // 启用启发式算法',\\n    '                \\\"presolve=1\\\",        // 启用预处理',\\n    '                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数',\\n    '            });',\\n    '            solver.SetSolverSpecificParametersAsString(solverParameters);'\\n);\\nfor ($k = 0; $k -lt $newLines.Count; $k++) {\\n    $lines.Insert($start + $k, $newLines[$k]);\\n}\\nSet-Content -Path $path -Value $lines -Encoding UTF8;\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_ey2oai8yczne3xdcCAam31N7"}}
{"timestamp":"2025-09-22T13:18:54.682Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_ey2oai8yczne3xdcCAam31N7","output":"{\"output\":\"�޷�����AddRange���Ĳ�����collection��(��ֵΪ��System.Object[]��)ת��Ϊ���͡�System.Collections.Generic.IEnumerable`1[System.String]��:���޷�����System.Object[]�����͵�\\r\\n��System.Object[]��ֵת��Ϊ��System.Collections.Generic.IEnumerable`1[System.String]�����͡���\\r\\n����λ�� ��:3 �ַ�: 1\\r\\n+ $lines.AddRange((Get-Content -Path $path));\\r\\n+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n    + CategoryInfo          : NotSpecified: (:) [], MethodException\\r\\n    + FullyQualifiedErrorId : MethodArgumentConversionInvalidCastArgument\\r\\n \\r\\ntarget start not found\\r\\n����λ�� ��:11 �ַ�: 22\\r\\n+ if ($start -eq -1) { throw 'target start not found'; }\\r\\n+                      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n    + CategoryInfo          : OperationStopped: (target start not found:String) [], RuntimeException\\r\\n    + FullyQualifiedErrorId : target start not found\\r\\n \\r\\n\",\"metadata\":{\"exit_code\":1,\"duration_seconds\":0.4}}"}}
{"timestamp":"2025-09-22T13:18:54.682Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:19:00.320Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Adjusting PowerShell AddRange usage**"}}
{"timestamp":"2025-09-22T13:19:04.396Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1101385,"cached_input_tokens":1028736,"output_tokens":17555,"reasoning_output_tokens":10368,"total_tokens":1118940},"last_token_usage":{"input_tokens":43190,"cached_input_tokens":42368,"output_tokens":530,"reasoning_output_tokens":64,"total_tokens":43720},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:19:04.396Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Adjusting PowerShell AddRange usage**"}],"content":null,"encrypted_content":"gAAAAABo0UzFq-GcQVc6J6lkUAG69lFaOe90pUurCpS-RRB7PISDv_5X8e2xxlsy3kAImyqh1ADxdPzC_KQCtpnKizDh2kIVKrJ7g1PF-rTZDszHE86KBfZZ5F1nQY4ybpXQ45sM2iWjyTabPF1YXmIuYZPYZ4sMiSicJ0l6JRj8rTAI-qSNepCnyFcHSlZQlSas4mpouw_Vz2W3LpBY1iOw1IOVMQLsCe0gS7pJfV6vr6zPmVK4lf9h6OjDNUZeIFp9YnChoVt2Jzbktnu8lTy1r8pJonM5qkvh-0Fas7H5_JpAq4hA2pC3TI1Lzltmb-e5Z0tHYENZXpUgzGXeXhyESPq3E8fbeNJDC4Gvfey3u9f2cZE-XRKtzb9-Tij5RUY5R76ro8Qb1O0qtVPiAagPiHcTOlBR11dqL7iUpiF7sk-XwlypgIiDaKitXpIjhM940aEXTR4h8FO_KVIPtHgGJoTHyUohblegpVgJN43JD6_3H6sCPaVy8v7SbSMSVqSZhOQbPoMNuz0gbZ53Hl5xGRqEnu1q4PPo5VPnocEjOJv-DQyIa9ilulzqakbAY1AAfme0KLwBjA3sVGSjupkAkyb-ShJhCPihDFQ6C7fg3uXQejN7X8ZThTl0WjpJwJZka0wZ_qPKAghYnzBNuWtOzB6SnzlVf97jGqt-SCub7nn3d_Dx_KoBYLfDUCnMZqbEnTC0gy1rnAlqehjkyWbVJImIgonOtEPx7Qy45kbVLgjRtK4loMWuU4q56SIynCCYP9JI2DgrXGNUfDvgz5sNGHcQYRbe5Bf8RxAqhmZzbcCdDJBSn_ySN0MtyoxybiI1NJ4TxTJsJNw1FNQT6OCpf2USYRAfX4Gq0angCeWtaqVJ488FW9W8-P9ogUGM59HZr0NWF8mYM6dGaopdxgrCf4BX6vaB0SVlgVr-GO20E7xYBb68ZXogDcWCZCEcqU7Wh-BN15p3thSrVjmF6044-28ifTUTPrDopE2cYTjXRPNTYewFM8z7oZCJjcHy3WBoVDkuM2ZOej_uvcrGASiSdARFJJn6StNHLBbLehEU5IfDEiW6bmBBcsFAQZd_2mUHKluxw06jr1VpIsBo0LNBFqBK1LI0F5Px0eE6FbjYkmnt_GUv9qL5QjXumn3YLgGwR8NpTyCf9G0XwhZUIJgnVnG0rowyV5S34okPsY4KEG7EeI1jMCfgnnMy4GPP7Sjglhoe7mVMULVrHUgZhAvdUfZccfHQpvhkS6GtUxo19JeviU2TYGhfcLAaDp9ODx-MTb6NbbTcQWBugn7NS4yaDum7QOmLZDSe4rHQo-k4xan5ZWWXn8hUsAc3GMmWOxJvLZgKmTIC9_n7Ur4g2vPOc8FF2P1uBaTA_Bb3OXuVVClpmNNm9kqX4igfaj8gyyYn0TfKeCqtyZ-L9SveMVEGS1dO0ox85timCYROkRY8ESJx9zMkwG9pvLx3o9py5H34JpB-tcZpErTitNXd_7Klg7leprfj7U6sfF-5Eq2D2CjLoTKyU-OV7EWMYl2Qus558f1icLq9I6nUCcBahL-yZ7avtrEcug=="}}
{"timestamp":"2025-09-22T13:19:04.396Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$lines = New-Object System.Collections.Generic.List[string];\\n$lines.AddRange([string[]](Get-Content -Path $path));\\n$start = -1;\\nfor ($i = 0; $i -lt $lines.Count; $i++) {\\n    if ($lines[$i].Contains('solver.SetSolverSpecificParametersAsString')) {\\n        $start = $i;\\n        break;\\n    }\\n}\\nif ($start -eq -1) { throw 'target start not found'; }\\n$end = $start;\\ndo {\\n    $end++;\\n    if ($end -ge $lines.Count) { throw 'closing line not found'; }\\n} while ($lines[$end].Trim() -ne ');');\\nfor ($j = $end; $j -ge $start; $j--) {\\n    $lines.RemoveAt($j);\\n}\\n$newLines = @(\\n    '            var solverParameters = string.Join(Environment.NewLine, new[]',\\n    '            {',\\n    '                \\\"cuts=1\\\",            // 启用割平面',\\n    '                \\\"heuristics=1\\\",      // 启用启发式算法',\\n    '                \\\"presolve=1\\\",        // 启用预处理',\\n    '                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数',\\n    '            });',\\n    '            solver.SetSolverSpecificParametersAsString(solverParameters);'\\n);\\nfor ($k = 0; $k -lt $newLines.Count; $k++) {\\n    $lines.Insert($start + $k, $newLines[$k]);\\n}\\nSet-Content -Path $path -Value $lines -Encoding UTF8;\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_Oo2ZYYp6oWIddFWLp3YJ3a24"}}
{"timestamp":"2025-09-22T13:19:04.397Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Oo2ZYYp6oWIddFWLp3YJ3a24","output":"{\"output\":\"\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:19:04.397Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:19:07.006Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Verifying recent changes**"}}
{"timestamp":"2025-09-22T13:19:08.311Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1145187,"cached_input_tokens":1072000,"output_tokens":17639,"reasoning_output_tokens":10368,"total_tokens":1162826},"last_token_usage":{"input_tokens":43802,"cached_input_tokens":43264,"output_tokens":84,"reasoning_output_tokens":0,"total_tokens":43886},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:19:08.311Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Verifying recent changes**"}],"content":null,"encrypted_content":"gAAAAABo0UzMgpX1Q3HG3Yeram7_xTJVd3wHvk7Kay376zmfONDYJv5G5SDOFz369RqtiXpKz2z4zK-o5BkdHVrKhfS9Jl1WOQqdDP0N4IiDOhEB4hWJL6Rlgm67XQdvv6pMH4QiHr9TRwIXhLXy9dB3Cii4VZ7j2y4MbhiCqrErOtlxjh1oP3AHrGTIIcBiMacAlsvP1UViBjLn9zbuR-QU7eIxwzD5xH5NjXHmQDFDGef-9cY9SbC3Sm2HcJtkl14rLqzjf4cZ_P1iwMi28Zd2V3p_kp9WSoXieQ7zqfzY_YtZm4SOcZ8OeyFryS5jsWlztZfKPeEWPr9iwWz_bOsRreCFFNwuZ395Adn3tsCmvjsSpfMUjn6OErQzjz8mYgu-WNuw_y1jOXBm9EzgDPgqy90sWNjs0DXfNSlk4FJ7prv5qW-ZzHUV47oE8PIzgfuZXh0Qjw9PywqT0bkqjUyFdxmT69wAIjMohGEvXvhkyYalvnO1899I6KuOwt4ePFllKbnO5zAPcLKvD0hHZPG3-S-Fp4TZJT8MbMIn497j9Tz7aNST2ArhM0zrLC0sh6ie_RKQthUcPEomje1c0o99aecub_zIghH2rMCVobJ6oUCIgRubFFWD4HU_7kGGMC8q_HvDezY3Ik2a-47HpxfRRs2oT7ObG5H9UIwmEfuBUjjCiTIRSB4YujIk3uE3uaSdSMrjCijLZQ6bYiz4Z72g_egmga6V448sn4IqlQNngvgcoubfd47bbUnJwbuOwrcCMLZBHkN3auR65sfgCWWsc9v5YBCd8s0oABjDg5RzXsx2b64rWfS45d4YbfKt-pBOTeoTxYO3B6sYwJO-Ejs4zKROseeQnvHTdIok5Q8nelwkyMVc-udZ1Hb8y6vg12NlhZ9JPRd9Em1i4PNNTURXG9pc65Qj0GmDEYXRSp6l1JnnUMAcQSI="}}
{"timestamp":"2025-09-22T13:19:08.311Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-String -Pattern \\\"solverParameters\\\" -Context 0,5\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_Z3FxNDCzEijuFglUCldfbHlO"}}
{"timestamp":"2025-09-22T13:19:08.312Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Z3FxNDCzEijuFglUCldfbHlO","output":"{\"output\":\"\\r\\n>             var solverParameters = string.Join(Environment.NewLine, new[]\\r\\n              {\\r\\n                  \\\"cuts=1\\\",            // ���ø�ƽ��\\r\\n                  \\\"heuristics=1\\\",      // ��������ʽ�㷨\\r\\n                  \\\"presolve=1\\\",        // ����Ԥ����\\r\\n                  $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // �߳�����ȡʵ�ʺ�����\\r\\n>             solver.SetSolverSpecificParametersAsString(solverParameters);\\r\\n  \\r\\n              _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n              return solver;\\r\\n          }\\r\\n  \\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:19:08.312Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:19:23.522Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Verifying code indentation and structure**"}}
{"timestamp":"2025-09-22T13:19:24.256Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1189294,"cached_input_tokens":1115776,"output_tokens":18328,"reasoning_output_tokens":11008,"total_tokens":1207622},"last_token_usage":{"input_tokens":44107,"cached_input_tokens":43776,"output_tokens":689,"reasoning_output_tokens":640,"total_tokens":44796},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:19:24.256Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Verifying code indentation and structure**"}],"content":null,"encrypted_content":"[encoded data, 4964 characters]"}}
{"timestamp":"2025-09-22T13:19:24.256Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git diff --stat\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_eqdXsMO1Cs602QMezbbPvuqz"}}
{"timestamp":"2025-09-22T13:19:24.256Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_eqdXsMO1Cs602QMezbbPvuqz","output":"{\"output\":\"warning: in the working copy of '.claude/settings.local.json', LF will be replaced by CRLF the next time Git touches it\\nwarning: in the working copy of '.idea/.idea.NPP.SmartSchedue/.idea/workspace.xml', LF will be replaced by CRLF the next time Git touches it\\n .claude/settings.local.json                        |  23 +-\\n .idea/.idea.NPP.SmartSchedue/.idea/vcs.xml         |   2 +-\\n .idea/.idea.NPP.SmartSchedue/.idea/workspace.xml   |  72 +-\\n .../Algorithms/LinearProgrammingEngine.cs          | 931 +++++++++++----------\\n 4 files changed, 525 insertions(+), 503 deletions(-)\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.2}}"}}
{"timestamp":"2025-09-22T13:19:24.256Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:19:30.583Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Investigating unexpected file truncation**"}}
{"timestamp":"2025-09-22T13:19:31.539Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1234285,"cached_input_tokens":1160448,"output_tokens":18590,"reasoning_output_tokens":11200,"total_tokens":1252875},"last_token_usage":{"input_tokens":44991,"cached_input_tokens":44672,"output_tokens":262,"reasoning_output_tokens":192,"total_tokens":45253},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:19:31.539Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Investigating unexpected file truncation**"}],"content":null,"encrypted_content":"gAAAAABo0UzjQFRYLy-neeSQKNHxvJdyO-s3FK3q_ujn46fcTUXQbuB-3mYUyRC80xYmu9oKTRpMYKM10DHCjCeNGZn6_5Xy6WwnuxUADvYR8k6_ov-OpjTzusvi4xhPevjMJu2-VUL2ObjSCWi0ii2EYz-BZ-W-lo8nQfwTVKwzESEz4G6oUdHx2EnhZM2CUbQtHivHzDsBsHpUHNsEqmcaNAu866dj743O3Z-KzW3IUQzQgs0UrJzQmOZaHFjHazWUoD-_xcgyD_tWW8WPW5Qa48gHOF8xzEIM5kqZ-aJGoOQKkB3R4LnUXw-suTEMLa28GHhz8U-FmR9ri00t1yicU8qqFXPjLzX70VmCHF6b7DC7x4iMYaXtCMqDuzgV-jkK_HBOsLEttPrbPU9Dw2NB7bZBXxtnITGAhKX-PwnLUIQYLG--UjFxVR6Ohpv_d6WqxpIBFzJVTtstRKHV1AE33hKXGhejelbjIYHkOOXxfvGXL2n8qkHI9UZvy1YRU5WliVHl_N7Gi2GAj2pIRSo8hiyK2lMhFRmTaE_mdooB1zV2XcFnE58fwPmcWLKoF7gS4wQKMiI40kJZJRpAcxYxEIHdS85yFEvHvEs5yKqri8IvR5FOjlE8jYrbLyR4sCJxH_778ABuLs441c_EdgNKBNyCaZgdbjk3lBNr8GzA8F8nR6fTaPv3NwhmXc9rrgRF34Nx5VlnpufdyqapnvpHZMQ4shR0QeCAtPgDAUIWCYTWFpyKyJtnTplURI3hF8liyOA3Bn45gKNgJ3KPkkqDZ28udg552xWap8bzfcuaMNLTCJpugnvwyvBr29KhC2enZfQD1AyxTtM9EwdAod22eyOvQzUnCoPbaqt5v15s_09KfFz6ubdS5tuWLVhU-rP3O0k9cJes50_-LHwlfH5Yk_gBdNq930YQtEsGhsLTj-vxycNusD8Ls10ZwLozNA8gDWvJqtRU6kG710Si8R2ef7w5hZ52htJJixizf8sspWFvXVIRTBNRbn4J_VenmRtaBL-_md1uGcK09nuMLngJC7MnUoCcalM01-aMTBcXdmwSCJ3sHjWAsu3YrTStz9sCCpD2osm1QkqyeEWXMRhlsWdU7gYypj44PmHr6d3XMDiFf798UgBXvs73J96_i_T-7ZR2Kvc7CsyE19hJDF5nC0QXtCB7rqa5LkOct75ssk-iR0_7jZNhpk0zrNrToHw4U1tJb9fwf0Lh74YEbhTkuPdXsS1kgwm5xxF6JHwost-F_UgnJh_4XJWLZDLBjQuwbtFu5u-o-e9f8o20Srit1ucZIOUYx_6pwaazEBg8b6ccrazM8Ca3OMJyzU6QBE3h7_i8S1kkXRpRSAU9xWGyApXLjN3sbjBTXmAtXql7OTdyPCNHAyDxRnWzNd2fZ3dSXDF3mmDHd0upU2DPHtmdcyDJ5VK-F0n_QwryU2xlGgGOam3QU6P8kQqypZgl-yiQNk5b0bNgzRM-t6F3SuYbUOnIGye0uU3UnA73scWVTespMwfQ3GLtdTbfoNdokMyq4dJirUCRp9eh91GC8eA2I7i-vB79_Hxd-8wLw_yCT8Mo2lfCEZTqk8nEG8EB8JKs03IUzo62lCfIKg-7qE5oW7JecYHOuOy34hwIQu_o3RN14p8JYtW_WqZ_tb_CKTv37Ede07pZmJxzOVCQcwsqNZVChUpIPmd7z3AXLFqiWALXhti6GDOmQSPwDsLiRkEeTmkkmIdDAXvw7n9oY0IYnXBsBiCbGvFgyR4aPexP1VY_d14Ha2PN14FDj5vbPalF7Cb-2OOX7JjyTSslGjtJu47vllYLGlwQz5Gi13nGpiTU3inwXNfcHPc_bepk2NZDMqhE3gUXzpVib4mrJDpkulSINe1z8lQS2U9GGBrBTYydICoSsqNpM1xkigDnwcKz_l_VlciWZHypLE33S5X5GphhvC5wwP3-aPO2Se3qaxrMYGkgNyg6fZwihRDeqYjCGTD9i4DuOoMAGpEmrjCLL9q7D9kplnajzBfEebGef8ORTkMqWIk="}}
{"timestamp":"2025-09-22T13:19:31.539Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"(Get-Item 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs').Length\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_yMoXxSiy96oWsWGGIRxd3vwl"}}
{"timestamp":"2025-09-22T13:19:31.539Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_yMoXxSiy96oWsWGGIRxd3vwl","output":"{\"output\":\"127553\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.2}}"}}
{"timestamp":"2025-09-22T13:19:31.539Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:19:35.002Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Analyzing unusually large diff**"}}
{"timestamp":"2025-09-22T13:19:36.145Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1279575,"cached_input_tokens":1205632,"output_tokens":18663,"reasoning_output_tokens":11200,"total_tokens":1298238},"last_token_usage":{"input_tokens":45290,"cached_input_tokens":45184,"output_tokens":73,"reasoning_output_tokens":0,"total_tokens":45363},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:19:36.145Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Analyzing unusually large diff**"}],"content":null,"encrypted_content":"gAAAAABo0UzojsMeCxHE9bqCaKtBDQDT4_5UOCrormN9l3VDyJUYeP_slHZPq4UdGYD9ATf7fr0LBAr1BHIiKE2DLoWfyhFlA5yYI9DwvskuYzZ-Hu2aSW0cFHl1Th-6g4pWq3Klmf7cMI1UN_RT0AYCWN9bqKEUmRJ1JXRnSZYrDCGqqiNqbqJ4v64Tv477PAaT-PYfP8ObJfQ3O0xWaS65TdBjDelecxFi0vdneMSKbYkAEKF7Eh1qvM6a7Wi8lmiCV-BDpU4OBNNUF-nIuyehtGW8tDw3-UmyP7OmHVEW216uG9EdRKeBYSgRU13UUiRm8BuWK9eKJzd9k7v_PPNfmj1W0cQbejVEslKF6MUaJK9sYdKo7GwJrDqhoaS8JuZMZqYVRqoeNsIgIXHyJdscDcuQzI1yfHJwdWhZTcRImLsCpQqeNiONT45lYYNhN-ubbcT3hbk9km6T4RPsMEBGJEcnri1d2s61O4qOaywqbFmAiBPlZwJoQSBf8MVfik4rkwxEDS6On_ufk3mdk5WjipHwWv06kZAliZJNCADsd8ZEIeuSrw2d3xq-nA9OdQbMOZ3pvRkT1ACmgIOAZ5HNxKAhG44p_OGt1NmZqqVfhrEIDcUX6y5Cmmk3WHnyKAE1s6b0ulEps7UGz8LLBAMvLoFEi99hRkUPaq6orPnoGwNuANMMdNgq2dhEIBiNUOY3kDgPCcgNkwvJ2sGKAt2uPuB_GYBAYsO24QCPsAkZz5MYIM7ium6wHqOYy1lUxt5IzxChiuxYXlOG36jhr80p_nfUWZZBGh1za0XrDhN473aAEGOhoyEwe2d5SRZUky5zM-XHA6MS8WJGS-IQ30WQE2cx96Xq5m0Hy5FwJP4Ph8pWscsL-gC85gz93RY_6FL9Odr1nRUGTTm3NArCAGKp_ydy0RJezNwfpnwc5Ft2v1w5pKVT0gScOxI4mTqppZSN1nfeMji9MuUM-gcqDGB6M7gyPVcc4jypyuWheyyRXkP5FRozW5eAq9sSKRsOUFNBrwFdXprUmDbf-T5WzWMf6qIgiI8KgZWHiJt-H-pCiA94pOOH_dFksqUFWr7CGbnplgj__Y5GF4GTnkJkhNTk8hol4UZAmCoOoanX6RlmobAOPnxHWkfAjDvZztpsxk-MWPxVOWRpMXgEhFsmekc18GCPFBHacQ=="}}
{"timestamp":"2025-09-22T13:19:36.145Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git diff NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -First 40\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_zGu5cltsdaZ1hYIptTZnsCj4"}}
{"timestamp":"2025-09-22T13:19:36.145Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_zGu5cltsdaZ1hYIptTZnsCj4","output":"{\"output\":\"diff --git a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\nindex ad3c992..099e123 100644\\r\\n--- a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n+++ b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n@@ -1,4 +1,4 @@\\r\\n-using System;\\r\\n+﻿using System;\\r\\n using System.Collections.Generic;\\r\\n using System.Linq;\\r\\n using System.Threading.Tasks;\\r\\n@@ -12,28 +12,28 @@ using NPP.SmartSchedue.Api.Contracts.Services.Integration.Output;\\r\\n namespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\r\\n {\\r\\n     /// <summary>\\r\\n-    /// 增强版线性规划引�?- 实现遗传算法中的所有复杂约�?-    /// 核心功能：三级约束体�?+ 多维度目标优�?+ 完整业务规则支持\\r\\n-    /// 设计理念：将遗传算法的启发式约束转换为严格的数学约束，实现精确求�?-    /// 技术架构：Google OR-Tools + 分层约束建模 + 自适应权重优化\\r\\n-    /// 业务价值：提供数学最优解，替代遗传算法的近似解，提升调度质量\\r\\n+    /// 澧炲己鐗堢嚎鎬ц鍒掑紩鎿?- 瀹炵幇閬椾紶绠楁硶涓殑鎵€鏈夊鏉傜害�?\\r\\n+    /// 鏍稿績鍔熻兘锛氫笁绾х害鏉熶綋�?+ 澶氱淮搴︾洰鏍囦紭鍖?+ 瀹屾暣涓氬姟瑙勫垯鏀寔\\r\\n+    /// 璁捐鐞嗗康锛氬皢閬椾紶绠楁硶鐨勫惎鍙戝紡绾︽潫杞崲涓轰弗鏍肩殑鏁板绾︽潫锛屽疄鐜扮簿纭眰�?\\r\\n+    /// 鎶€鏈灦鏋勶細Google OR-Tools + 鍒嗗眰绾︽潫寤烘ā + 鑷€傚簲鏉冮噸浼樺�?+    /// 涓氬姟浠峰€硷細鎻愪緵鏁板鏈€浼樿В锛屾浛浠ｉ仐浼犵畻娉曠殑杩戜技瑙ｏ紝鎻愬崌璋冨害璐ㄩ噺\\r\\n     /// </summary>\\r\\n     public class LinearProgrammingEngine\\r\\n     {\\r\\n         private readonly ILogger<LinearProgrammingEngine> _logger;\\r\\n         private readonly ShiftRuleValidationEngine _shiftRuleEngine;\\r\\n         \\r\\n-        // 求解器配�?- 增强版配置支持复杂约�?+        // 姹傝В鍣ㄩ厤缃?- 澧炲己鐗堥厤缃敮鎸佸鏉傜害鏉?\\r\\n         private const string SOLVER_TYPE = \\\"CBC_MIXED_INTEGER_PROGRAMMING\\\";\\r\\n         private const double OPTIMALITY_TOLERANCE = 1e-6;\\r\\n-        private const int MAX_SOLVER_TIME_SECONDS = 600; // 增加�?0分钟支持复杂约束\\r\\n-        private const int MAX_CONSTRAINT_COUNT = 50000;   // 最大约束数量限�?+        private const int MAX_SOLVER_TIME_SECONDS = 600; // 澧炲姞鍒?0鍒嗛挓鏀寔澶嶆潅绾︽潫\\r\\n+        private const int MAX_CONSTRAINT_COUNT = 50000;   // 鏈€澶х害鏉熸暟閲忛檺�?\\r\\n         \\r\\n-        // 约束权重配置 - 对应遗传算法的三级约束体�?-        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 基础约束严厉惩罚\\r\\n-        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 组合约束重度惩罚  \\r\\n-        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 业务约束中度惩罚\\r\\n-        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 公平性奖励系�?+        // 绾︽潫鏉冮噸閰嶇�?- 瀵瑰簲閬椾紶绠楁硶鐨勪笁绾х害鏉熶綋绯?\\r\\n+        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 鍩虹绾︽潫涓ュ帀鎯╃�?+        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 缁勫悎绾︽潫閲嶅害鎯╃綒  \\r\\n+        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 涓氬姟绾︽潫涓害鎯╃綒\\r\\n\",\"metadata\":{\"exit_code\":1,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:19:36.145Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:19:49.374Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Handling encoding corruption**"}}
{"timestamp":"2025-09-22T13:19:50.170Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1326441,"cached_input_tokens":1250944,"output_tokens":19241,"reasoning_output_tokens":11712,"total_tokens":1345682},"last_token_usage":{"input_tokens":46866,"cached_input_tokens":45312,"output_tokens":578,"reasoning_output_tokens":512,"total_tokens":47444},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:19:50.170Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Handling encoding corruption**"}],"content":null,"encrypted_content":"[encoded data, 4300 characters]"}}
{"timestamp":"2025-09-22T13:19:50.170Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git checkout -- NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_GSlInskmqxR58VXPR54p2LOi"}}
{"timestamp":"2025-09-22T13:19:50.170Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_GSlInskmqxR58VXPR54p2LOi","output":"{\"output\":\"\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.2}}"}}
{"timestamp":"2025-09-22T13:19:50.170Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:20:01.718Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing Python script execution in PowerShell**\n\nI'm figuring out how to apply a targeted file modification using Python called from PowerShell, considering encoding with BOM and Chinese characters. Running inline Python script via PowerShell's standard input is tricky, so I'm planning to create a temporary `.py` script file, write the Python code there using PowerShell’s here-string syntax, then execute it to safely handle encoding and string replacements in the target file."}}
{"timestamp":"2025-09-22T13:20:02.220Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1373939,"cached_input_tokens":1298304,"output_tokens":19713,"reasoning_output_tokens":12096,"total_tokens":1393652},"last_token_usage":{"input_tokens":47498,"cached_input_tokens":47360,"output_tokens":472,"reasoning_output_tokens":384,"total_tokens":47970},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:20:02.220Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Preparing Python script execution in PowerShell**\n\nI'm figuring out how to apply a targeted file modification using Python called from PowerShell, considering encoding with BOM and Chinese characters. Running inline Python script via PowerShell's standard input is tricky, so I'm planning to create a temporary `.py` script file, write the Python code there using PowerShell’s here-string syntax, then execute it to safely handle encoding and string replacements in the target file."}],"content":null,"encrypted_content":"gAAAAABo0U0CuBi6wNrvOtXdXasSBoJz4Rb_oJr2kweDUkdjRRFqiYibG4tc_NyEWonPeL5838oya80yQUnHeYPkx0U9rKB1dIhHKhPoqcycf_plkPpqgWUFr1WTz0oQJJl_Jh-uHN-mjWZdcMD9l465RHdP_Tld3okTiqR3SSenx5am7A9k59Ob9MCUIzctycdO5s6RzubpychrMRm0_a75kIZiC4Iyi0QW97CEKuYr7E6MZLsN2lRoel7t_6-tbnw8fhi1Wpo6wsRLYYj2Dhr-f1L7eIARQZ_8wIdGhCj3QKOapQ5X4KW-QrHVBIEhDNFebAkQi15hwZqyhhjrDQih0ySB7VuzQyeIvv6dGJSR-oSHyvEbnN26pg4ofbidb65e-paroWDbwfCeJkQjamQTyQ7ZyA-fnWWN3Dxs338d5S-1KoDBSOVGZrlmE_dv_Z-p9-U8mx4YruyfcxMtX1M6jC7-0OhDJQ2T4rgYCfmuGzc5S6KrG3XApuvTBoN6jDMx38rjVnuCAu5465rd7a4okN8-89ktCQ27DhvEQgpjBc7OAf5PRqPgh7blbHgFHl92_IAZukkPfLrNduQSWDxgkUu-z58YFbHMRYSGay4oczBu1rMIjH1f8h89awpUl8pBDq1z2CC36oYKdv-NEXO5aHlKM2hj7j5HvaoKbk3_NgS4hk5QHAiRgtH-H2JnNtHeyvnpnhFmMxM6kW-CIEjFPBun_8GxkgSNeSNolHxpk7V_m9EKmQH2M-IZnlH6z7eQGEQw1QYS7r4nUNVUS6SVA-MPYMGQyME9umwvFfkF-LwuyUAQ59xzdou64QxehYZtx81eklZyLpNK7du3nPA9EKG9e2eJKyBsX_SoQMmIWK6NyUPpuhN3X_-15MP05MJx765JLr-N5QHmY4ad7vOKJPHFMdnLR1lNrm0-gwc50bwS3jssR0TBr6UFbeZOl6a2I5IMoS5n9EnZGPG5HVwdbxKiBVycbE_KGkDkHlUt6p0zx8NLk-OlBkFYgBzefI88qwGYnfvKi7YXzZXCkubsvn2GoO1qqa28hzxgYkrPIFU4S56OKBrDKH8jLZSKaJuREKQSxlV5sLtEcg5_NWXk1xwlVqNmfG0QBlS4cFTwECezIpXt6krOAUz5uCPXNAblT5ZwbLAfypCpuMI8u9Ou06LSsbOQo3oBQ478Twb_y-ncAY0cXs-brBuAa12raigbVofRfWUGXwvO1J_n2FfvLauliBKhuQoEPfVkrVOXcFIHGGc3CtFvfM8C9HFaVeKwx1v1BYchvOijr9zOWhLnZUhO8PlkBwUuYQ9IYqsnOazZ7CTePVzkf2LTwZlaMEQHp5bcOBCfNvONgV5ohWzIKLWAPLZ0Rnh_uq_munrBP3Q9_O28tTznGH4udSX9AymRoZlx2FKszFYUsVMij1-vJ0o5p7bg3tn28AiHl4ym3K2ecPeR00kg01TS49AtHiTVWmGmTY6DHXNzQbVcMwEQp1Cvcp1fFMa-zY-M9hGCnc2nW5EAARhbL1kR45oPiPnp1O0pgxDl4dKzy5CRmhUqjmNwK0TP4ABK8pW3oL0vFVe2fDnKYoHSpSplUWl4871VEAGtueqFS5_YjwzD4Ty4MHJbShlrNgiXFyZegRq-rX73lNr0cXfyfyM89r84UrwCIKBKTnAnrxFDriHiEU20HL1icqhkGMi3nogoH3_jByh1WhvrOkaStGSz5RflwvqSp6aYBYCt0oQ33mwx74sEMno2ZA2xN6u2BTZeAadDfQyTGN68u_kWH7pR3hzSrz4iHd7cJu6lW7JX63-nCYGfQDX4_h9zVEePW0miZJ5Jbz1QsR9aAbgDVf5VidUVFNRmzWATbpR5ywQ-kIr72-6IQ4KmNhw9lgA-tOKu8H1cxc85Lb9pwJnCPYz8xdLyDxXMRlkruIO0tgqRGpVXuVwPVHq6yyFIB0uF989OuJs_lvpOTnmpXuJXifwNjlPRo0WIISraghE3UWNNqaEJ5RclxMKUbuw-f3Qupx3BCf0hE6dPskqMxZ2eDIH89LUiLLKENmZdrl49CdY-omAETXJMIYjWBHfNRdvx_jguPMh0TW5EB4uc1_NrRVf2j8vVnVBR_Uggm7bJykrOZQdZvRlm_Pw1CXdoee6dzAhdmqcZpckeiGmuoON1RxQz5Dtx4zataGNFgPzr2t421giWfQWY1J2l_lg6SOfR93mjb95fD2vM_tKSGtUNbFq6A94b7aV0fhwvz8Z50PboWdHYLtPdFawIX7tV1YrpeVhh1XpgEWBdy-eh-fj0G64xKXd9BGWoYG7PfEw9v-xkEtkxgkKNxLOtrJiCEAca1jpKl0-VRurFpF_qeP3agIwx2SV_PTOkV9yhQDvt3YXdSjUUnvnwJW2kxk-2-UtNBWqQgLtgWVPFJSMMPASrHyPI_RtZPNiZvl7Lfn_GheqWxcMBXquwDAljLZhMjF7_mpmpsEwwzo0Bdp8MwrJpiJX87InsFNIBcXz9YlXecFispctmbMxmsQSGs2__sZUgv46aKd-fJxPR7HmcZVvc7B7cHNyk9v0JJLTWQCoYie2-QrP4kmMDf8IKdSmGpXnlWNSzq2gwZfzP-lwKZuJQz6LMDYY3FdCOGR6XTVQwRT9nZZalor0R4Ssa9R2nEZDO-p5XPI-7vTPyWnrA-mit_I-ShCmeXxjyZWELfAQiYlmwLtSggldSvI4hqDNhJoI78UiKwWbXH8TQzBKURuUeym4mEp6oMegx3g0xGMZLISW69HQuQ-psdV9-tTNmW0_2rD6qIElbuMbB8Qku_XYD05aE9P_TJ9SKco6kR9hjZKcY-hpEzb6BMqFcaJfU0fe6JqOPdAe4tgt1qEp_3GAVK6_vOAMAxhrpSAuHJD-ZKM_KGi-aITpzobm3AHQFDjSwuSTELEkdPtNuOs7jbjPqRIWvBAzkNIgXxA-JAWwCkvGwLcf-DHc3cEJotg9hqNw75eLP4-n7llkYRYk_zFPRli1nb18VMN8cqWPS5tsWqy0T7DpGnsrZ38RPfdSIs7Dag5kmcHJSJIDEPOjKa7GA1F3m_qNYYHkC3L3911UTnJ6_0NB9dKP47quAIXLdJNbRNWVfch-zlurDWYPNLgepr2t95XDDrcOA7mpq4OkWdcW0R-rkn9zqFUbafF-VEcMwjLZhlTeeRpbt9Js6N7RixalfRT3xXNUU_IxqJiZfPpgp5c39pLOgpbDcyyOsG9Lf1IndZ33teEaHwAV9YddpqsiRyCcZQnIQNqHtv91AGWtCKGWMTBlxJVOCLwR2z7MavM4f-7LEGaATcl17MbmDFkaZfRLGp7DKyDmew5hG2WFM_q2Gi1pBTViH-5MXkqHp-wohqm2UnyOihLgjmaY="}}
{"timestamp":"2025-09-22T13:20:02.220Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-String -Pattern \\\"SetSolverSpecificParametersAsString\\\" -Context 0,5\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_kTT6eZNTUILPZTXUgErnnMFs"}}
{"timestamp":"2025-09-22T13:20:02.220Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_kTT6eZNTUILPZTXUgErnnMFs","output":"{\"output\":\"\\r\\n>             solver.SetSolverSpecificParametersAsString(\\r\\n                  \\\"cuts=1 \\\" +           // 启用割平�?\\r\\n                  \\\"heuristics=1 \\\" +     // 启用启发式算�?\\r\\n                  \\\"presolve=1 \\\" +       // 启用预处�?\\r\\n                  \\\"threads=0\\\"           // 使用所有可用线�?\\r\\n              );\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:20:02.220Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:20:30.007Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Crafting regex-based replacement script**\n\nI’m designing a Python script to precisely match and replace a specific multi-line C# code block using regex, carefully preserving indentation and Chinese comments while maintaining line endings and trailing blank lines. I’ll use a capturing group for indentation to recreate the new block with exact spacing and ensure UTF-8 encoding is correctly handled when reading and writing the file."}}
{"timestamp":"2025-09-22T13:20:32.809Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1422096,"cached_input_tokens":1346176,"output_tokens":21604,"reasoning_output_tokens":13504,"total_tokens":1443700},"last_token_usage":{"input_tokens":48157,"cached_input_tokens":47872,"output_tokens":1891,"reasoning_output_tokens":1408,"total_tokens":50048},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:20:32.809Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Crafting regex-based replacement script**\n\nI’m designing a Python script to precisely match and replace a specific multi-line C# code block using regex, carefully preserving indentation and Chinese comments while maintaining line endings and trailing blank lines. I’ll use a capturing group for indentation to recreate the new block with exact spacing and ensure UTF-8 encoding is correctly handled when reading and writing the file."}],"content":null,"encrypted_content":"[encoded data, 8356 characters]"}}
{"timestamp":"2025-09-22T13:20:32.810Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$script = @'\\nfrom pathlib import Path\\nimport re\\npath = Path(r\\\"NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\\")\\ntext = path.read_text(encoding=\\\"utf-8-sig\\\")\\npattern = r\\\"(\\\\s*)solver\\\\.SetSolverSpecificParametersAsString\\\\(\\\\r?\\\\n(?:.*\\\\r?\\\\n){4}\\\\s*\\\\);\\\\r?\\\\n\\\"\\nmatch = re.search(pattern, text)\\nif not match:\\n    raise SystemExit(\\\"pattern not found\\\")\\nindent = match.group(1)\\nnew_block = (\\n    f\\\"{indent}var solverParameters = string.Join(Environment.NewLine, new[]\\\\r\\\\n\\\"\\n    f\\\"{indent}{{\\\\r\\\\n\\\"\\n    f\\\"{indent}    \\\\\\\"cuts=1\\\\\\\",            // 启用割平面\\\\r\\\\n\\\"\\n    f\\\"{indent}    \\\\\\\"heuristics=1\\\\\\\",      // 启用启发式算法\\\\r\\\\n\\\"\\n    f\\\"{indent}    \\\\\\\"presolve=1\\\\\\\",        // 启用预处理\\\\r\\\\n\\\"\\n    f\\\"{indent}    $\\\\\\\"threads={{Math.Max(1, Environment.ProcessorCount)}}\\\\\\\" // 线程数量取实际核心数\\\\r\\\\n\\\"\\n    f\\\"{indent}}});\\\\r\\\\n\\\"\\n    f\\\"{indent}solver.SetSolverSpecificParametersAsString(solverParameters);\\\\r\\\\n\\\"\\n    f\\\"\\\\r\\\\n\\\"\\n)\\ntext = text[:match.start()] + new_block + text[match.end():]\\npath.write_text(text, encoding=\\\"utf-8-sig\\\")\\n'@;\\n$tmp = [System.IO.Path]::ChangeExtension([System.IO.Path]::GetTempFileName(), '.py');\\nSet-Content -Path $tmp -Value $script -Encoding UTF8;\\npython $tmp;\\nRemove-Item $tmp;\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_eMABWQPEcOuacK584RvgQN79"}}
{"timestamp":"2025-09-22T13:20:32.810Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_eMABWQPEcOuacK584RvgQN79","output":"{\"output\":\"python : �޷�����python����ʶ��Ϊ cmdlet���������ű��ļ�������г�������ơ��������Ƶ�ƴд���������·������ȷ��·����ȷ��Ȼ������һ�Ρ�\\r\\n����λ�� ��:27 �ַ�: 1\\r\\n+ python $tmp;\\r\\n+ ~~~~~~\\r\\n    + CategoryInfo          : ObjectNotFound: (python:String) [], CommandNotFoundException\\r\\n    + FullyQualifiedErrorId : CommandNotFoundException\\r\\n \\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:20:32.810Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:20:56.934Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Preparing PowerShell regex replacement script**"}}
{"timestamp":"2025-09-22T13:21:00.602Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1472294,"cached_input_tokens":1395712,"output_tokens":23287,"reasoning_output_tokens":14720,"total_tokens":1495581},"last_token_usage":{"input_tokens":50198,"cached_input_tokens":49536,"output_tokens":1683,"reasoning_output_tokens":1216,"total_tokens":51881},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:00.602Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Preparing PowerShell regex replacement script**"}],"content":null,"encrypted_content":"[encoded data, 7588 characters]"}}
{"timestamp":"2025-09-22T13:21:00.602Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$text = [System.IO.File]::ReadAllText($path, [System.Text.Encoding]::UTF8);\\n$pattern = '(\\\\s*)solver\\\\.SetSolverSpecificParametersAsString\\\\(\\\\r?\\\\n(?:.*\\\\r?\\\\n){4}\\\\s*\\\\);\\\\r?\\\\n';\\n$regex = New-Object System.Text.RegularExpressions.Regex($pattern, [System.Text.RegularExpressions.RegexOptions]::Multiline);\\n$evaluator = {\\n    param($match)\\n    $indent = $match.Groups[1].Value\\n    $innerIndent = $indent + '    '\\n    $lines = @(\\n        \\\"$indent\\\" + 'var solverParameters = string.Join(Environment.NewLine, new[])',\\n        \\\"$indent\\\" + '{',\\n        \\\"$innerIndent\\\" + '\\\"cuts=1\\\",            // 启用割平面',\\n        \\\"$innerIndent\\\" + '\\\"heuristics=1\\\",      // 启用启发式算法',\\n        \\\"$innerIndent\\\" + '\\\"presolve=1\\\",        // 启用预处理',\\n        \\\"$innerIndent\\\" + '$\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数',\\n        \\\"$indent\\\" + '});',\\n        \\\"$indent\\\" + 'solver.SetSolverSpecificParametersAsString(solverParameters);',\\n        ''\\n    )\\n    return [string]::Join(\\\"`r`n\\\", $lines)\\n}\\n$newText = $regex.Replace($text, [System.Text.RegularExpressions.MatchEvaluator]$evaluator, 1);\\nif ($newText -eq $text) { throw 'pattern not replaced'; }\\n[System.IO.File]::WriteAllText($path, $newText, [System.Text.Encoding]::UTF8);\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_Ng6Bh9POKR4a8xjoRljYR8y7"}}
{"timestamp":"2025-09-22T13:21:00.603Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Ng6Bh9POKR4a8xjoRljYR8y7","output":"{\"output\":\"\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:21:00.603Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:03.661Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Checking code snippet**"}}
{"timestamp":"2025-09-22T13:21:04.285Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1524226,"cached_input_tokens":1447040,"output_tokens":23371,"reasoning_output_tokens":14720,"total_tokens":1547597},"last_token_usage":{"input_tokens":51932,"cached_input_tokens":51328,"output_tokens":84,"reasoning_output_tokens":0,"total_tokens":52016},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:04.286Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Checking code snippet**"}],"content":null,"encrypted_content":"gAAAAABo0U1AgjL0sWK3ad2o5j6J-8HUDlTvrE2gvBeSzAWXR3fZ-Phs4NVmQ1X0APfdOiz0HMdpq_yTnekiDgzhT8EqbE9yhZ_6DbGYPLD0jqfZojfs1DZdF3P57tidYAf4vc-SVeB8lFawUYsJLxro7_5Tn9RI3BHmUkWfNhbuafIEiZywfmr5evZOTLJghNWbQmSXvRN9PtEFXuN32Dot03BoLtx_K7vGL7-18l7bFE1Sj8m0gbEOykgLmA6leh1zVlwryKzVBz3fSMKHKOgmOHgExOkGdn9YKAWQecKop5yj6ZDrSumc0jheH87Nxb6OLN9THiRAi1umvG3OyIAsHc609hlEPv-m25G0xnX34a3rIKmc7lV0bUM_AHZgYz_Oy89oFjla3WX31BvuVuzywxNeYXCwljJWsttfGEgsdkYOm5EqqBxhsqJIJjRBcHZ9c88A73rR3XbctCQE46mUh9TLQpw4D5lqtM1PQ2l5Kbe-shml-J7Q3Vre2A20cKpoc1BOYrulMAWGX5YWd60Je4UojpDGCrhFYg8vluGPqQK-Ky1Ow0WfBpJPePh2jkCMzDxSft3y3MQWxzOJrpR-X14AF4DcQZR_Omn6BOYqWMFy5L5fGqD4siO0GmDQ6FG_7UC0jlG_feYRmNaS4SKV4f3dCMZ5nLtSI8gIe07BjJM6zPv5JxGXs_pRZFDaD2DmKLW1DfeiaesurQ1WGNhwdZSGwATVHIURwquSGUz2MxnYnMIs4Nb7vS3APN3_JimENgjV5IqDioS1TEcz51B_apmHnlICVjYwWEuiNr6DELtq4dp0hBk7hvPCFkphZ3lLTnnlxvg5L_e7ic6an_OP6YE-OkkoouyFhFz4xd-bKcS6BPzVsgNPnBgIag_StyBVbjym6sQs"}}
{"timestamp":"2025-09-22T13:21:04.286Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-String -Pattern \\\"solverParameters\\\" -Context 0,5\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_JEga8r31tiKI5rNYQuo8R7YV"}}
{"timestamp":"2025-09-22T13:21:04.286Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_JEga8r31tiKI5rNYQuo8R7YV","output":"{\"output\":\"\\r\\n>             var solverParameters = string.Join(Environment.NewLine, new[]) \\r\\n              { \\r\\n                  \\\"cuts=1\\\",            // ���ø�ƽ�� \\r\\n                  \\\"heuristics=1\\\",      // ��������ʽ�㷨 \\r\\n                  \\\"presolve=1\\\",        // ����Ԥ���� \\r\\n                  $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // �߳�����ȡʵ�ʺ����� \\r\\n>             solver.SetSolverSpecificParametersAsString(solverParameters); \\r\\n              _logger.LogInformation(\\\"?? �����������������ɣ����ã�ʱ������{TimeLimit}s�����߳�֧��\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n              return solver;\\r\\n          }\\r\\n  \\r\\n          /// <summary>\\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:21:04.286Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:09.920Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Checking encoding issues in output**"}}
{"timestamp":"2025-09-22T13:21:10.825Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1576472,"cached_input_tokens":1498880,"output_tokens":23502,"reasoning_output_tokens":14784,"total_tokens":1599974},"last_token_usage":{"input_tokens":52246,"cached_input_tokens":51840,"output_tokens":131,"reasoning_output_tokens":64,"total_tokens":52377},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:10.825Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Checking encoding issues in output**"}],"content":null,"encrypted_content":"gAAAAABo0U1GiVRdQstBOI1xXUTG5vFzQAdiJfuSQWmIkkU9XYZn5R2uHjrG5epz2hL4sfOACXx3YuKZ7UtaXN42ew1QVbZZIREIkGsLQ9zS7WfafKdrfxav3Vr9hsO6-daZ4lUIhpjv_9qzHa0GjVdfTXwRhZdxDbErnC3aFRcgSO3aUlK-8HT-p0vuaKJ7zqbFo9NZ1xLhrJILQrjDu5lBHd3XEhWr_-kMqSv4hIO1qA4ybBQjh2MJj2Vpc-Ej-mw2W-S9t69C8CNaTu0nWS0Kf6K9YQSUh45bMhF4XTM3a4lQmFf9CE5hloXXtutGOcuMWEKL30EEaR5245jlXgLVQkotR2UgmTgDHmxjqwxGzn2DJ1a4jfZJtabpLaoi2gbe1Nq8pWp6iCN0sO_yYRkDfq6B6TowDsYCXhfolykbVGQbyx55HZt0o1a3ihR5txi0hBdXtucMERLv3je75JXpQ7geXxjXXfPQKwNJQfmFIELy-UV_XJ07VQ6rbLbPu2Y8Hc7KsxRKfuHKHIQJ32Huk32p75v1nFfnlagv0DDeBLdqwR9eMhRy_C3mWzwHS_-HFjc8-BgI1dzwO1y3a0XYceInecuX5EmIBO4vbK9jTBoyOlUD3nqElNBNUW_XvwxJzjcDhSOf95JAf1RC7GodeewVBcF4KoFZLE39lqTA9P0DsW2gWYyStmcUid2FA0O2j1Zn9eC6ygGpBo7BY1JNyLNDpyduRwJsRIB8M0VLXGWkONukMPJdfkalDP0w4ndNyBegP_aZoWCCGPS2EnZit3BQGFxIqUkWFV2XoxqUyzWLDxAXsiwGslTbDMtudftyUNGX63d4pGkW0j4q7MX6Nf1nJUQ_C3YPnS8Cl93rlPAUAyHlW6ooNwEFQI47IxS-4ynvWCgvobCIHSkkzD8idJtLuLq4xW5V58xOXjNnVJLAjrd4n2C4ZdfzcQZ4Q09X13nG-GEcmtxWi6N3E7gM6YsnvOy9cuhIAAalskc70ehngTpS69Y_SRnIPOZcd8uLVvAPHFGuznHgmrApYTZdLJL7EeYytgnSqihmxI2uO07JmeuytNkdPuTz0ixH0BhvvT3_gAw5KRzZzpMeu-pyYq0i_71NrGZa73HfKqKqHJCYHdVJ1Nhdbc-eJKbVbNfR5r5KEARr5Md0efV_Mu7hwoxko7ksep5j0MLE44oG40T5BIxZbF6DlH3Y1Znc4s6QY5SeGw1ZVCMSqmYh3CKpZSCRN4zptDCmNy0r-wo0eB4dEuYG86aca5sPv2FOCKlbucUUwW1GBCDNhysq5_IS53cuNN51cZ0pga2Oam4PNsJkN4_QGImbVwkOJlDKqX-DmfRjzsD-TlH_KeXMXPrKot5UtukfQU828-lmw5spEVkfW0p3qf7M0RshB-P8dzfKntDKfJ2idr62wncn8G6G3qikBgu_prCsPh-fj--yUn4Ojxr3nS3pNXgTQHqyTdYTtjanQA9T1FepjGFJlbZzbRNb7_zZyfGUNKBJuRoXT4_Zeoq66oPQC4S0q5viRzIqyj0boGdsdlAEyatyGXhft4wy7WEA1AsPf7LgJVYLP_4-VReND_X_gVDwhZm4YuJ7HJefXAUy"}}
{"timestamp":"2025-09-22T13:21:10.825Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git diff --stat NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_EnYhOVBjaDEsJJk2WfTDKsu3"}}
{"timestamp":"2025-09-22T13:21:10.825Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_EnYhOVBjaDEsJJk2WfTDKsu3","output":"{\"output\":\" .../Integration/Algorithms/LinearProgrammingEngine.cs   | 17 +++++++++--------\\n 1 file changed, 9 insertions(+), 8 deletions(-)\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.4}}"}}
{"timestamp":"2025-09-22T13:21:10.825Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:13.495Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Reviewing diff details**"}}
{"timestamp":"2025-09-22T13:21:14.690Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1628974,"cached_input_tokens":1551232,"output_tokens":23575,"reasoning_output_tokens":14784,"total_tokens":1652549},"last_token_usage":{"input_tokens":52502,"cached_input_tokens":52352,"output_tokens":73,"reasoning_output_tokens":0,"total_tokens":52575},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:14.690Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Reviewing diff details**"}],"content":null,"encrypted_content":"gAAAAABo0U1KSXm4AyCPZ-ej93sCt0sZx9_MODWaATbb6yu7Ktz8T5GDvQRMfrF0Nu4j4HybpXSedyT4e7MK5bU5zJx87XyYdFC_JE6pjOaDrHw3ZgNyiJ1Q71R7zEu5kVIM6uz83nDupv9hSSBGPcxVa52FqhcZ4HnrZJq6ZEGae_dnma0xDrrq1AkGarwel8Wi_qADLNmZtrMFL-CXPgmpUMuzx4sDCzgfk2D9XjiifuZ3dJN3l5Et4W6L5U_xWXe-Y3KY4QzIld_TpYRbcYa4SwKPRPgNAEjpYFutoUacc0dZpOLbioMGgmE0j6ypNKbqV10E0zSgBTzmZlTxUFCCwJA4mfv-Kj1_9rmLsPnfD35eulAXs0xC_Q2KLKqPYxcqFxf6pqWMm_x7UK229YoL_E6TgX2HdJG07IOU-jCu4cg5QMfwvteFeyeEpIwDhxUZXAxcJueZ68SmQDSfnuEl9D2tb8Y62jrvyAIyz76MRsVh-mWzr8zOw4ihuTiZID6Xl4Pqolbir8OsGhLCfp3195cZPskLK9cWn_9zZFIw8SBdgwZmpTZAeYlGWDHwk1KKTX7x7NPvImfZrZRY2QxaX62tJzDRBCK5Vs4cP80wkAQ_p8J15AS9TLugKDATXI9V5S_Gwy7GLbbTbDyyOmP90XsWxo1QxlPAxSTcC0Av-6i09IlXyB34txGRnGuWIB1qHu1KzKoPlsPwKce538S4-6JrCNlsyJCCcVTn0Fp4qnmDts_v09bX0-nXrLzGQDFwzWLz_lJ59U7HAIg5VYoMTMflkbqMN4ZxzzBRALKyGATqew3Ed2E4LbaTAdnM1ehHSwl_Gk9UrByM5CydNxIDkhpa5lO09i2zMYnrF0soN57oPQqICNtJKKJTJU9IV5dnrcn_5totkGIMir_9uAREVlEER8NSQ4b16LwGxyYWvnlGBru64EOAjM5mhYkUjOFm2c_x9DXgcbBRM2TStVECCdhgB_oE_A=="}}
{"timestamp":"2025-09-22T13:21:14.694Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git diff NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -First 60\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_yOquAXs6IOYDjTNmAAxKC75p"}}
{"timestamp":"2025-09-22T13:21:14.694Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_yOquAXs6IOYDjTNmAAxKC75p","output":"{\"output\":\"diff --git a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\nindex ad3c992..be76f05 100644\\r\\n--- a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n+++ b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n@@ -1,4 +1,4 @@\\r\\n-using System;\\r\\n+﻿using System;\\r\\n using System.Collections.Generic;\\r\\n using System.Linq;\\r\\n using System.Threading.Tasks;\\r\\n@@ -121,13 +121,14 @@ namespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\r\\n             solver.SetTimeLimit(MAX_SOLVER_TIME_SECONDS * 1000);\\r\\n             \\r\\n             // 设置优化参数\\r\\n-            solver.SetSolverSpecificParametersAsString(\\r\\n-                \\\"cuts=1 \\\" +           // 启用割平�?-                \\\"heuristics=1 \\\" +     // 启用启发式算�?-                \\\"presolve=1 \\\" +       // 启用预处�?-                \\\"threads=0\\\"           // 使用所有可用线�?-            );\\r\\n-\\r\\n+            var solverParameters = string.Join(Environment.NewLine, new[]) \\r\\n+            { \\r\\n+                \\\"cuts=1\\\",            // 启用割平�?\\r\\n+                \\\"heuristics=1\\\",      // 启用启发式算�?\\r\\n+                \\\"presolve=1\\\",        // 启用预处�?\\r\\n+                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数 \\r\\n+            }); \\r\\n+            solver.SetSolverSpecificParametersAsString(solverParameters); \\r\\n             _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n             return solver;\\r\\n         }\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.6}}"}}
{"timestamp":"2025-09-22T13:21:14.694Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:23.166Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Handling BOM insertion in files**"}}
{"timestamp":"2025-09-22T13:21:24.538Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1682062,"cached_input_tokens":1603712,"output_tokens":24023,"reasoning_output_tokens":15104,"total_tokens":1706085},"last_token_usage":{"input_tokens":53088,"cached_input_tokens":52480,"output_tokens":448,"reasoning_output_tokens":320,"total_tokens":53536},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:24.538Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Handling BOM insertion in files**"}],"content":null,"encrypted_content":"gAAAAABo0U1UXtRSDD4qV-V38G1XPov1lcbPVDghbEy5t3uk6bOW98YJ9yNYdvHmdJuPyeOqOng93CHdN9Fx8G8SelreGk77ShLI5_MCdL5krGpiXerpG9GwkS1ie7HMstFwwBjkxDFwOq0L47-F9Pk8Q_9YJbbUzoBgbw1cMX9q7jq-l9_U6UFU67-Tp3bveTMPj_QjZMtCOf9AGXpeqXcBRu4JpfAqpTLAG905GBjHodYAf5NZMxawYjcY6WD6Iyq-E4g7Wqazbnva6j09A0U3t4C12LRMVs_aDwYyoiA087-G5CVAVMLrlgDAndACM8_Dr5egxwuuWt_zdv8huOuOpp0Qn7Q3rK9oq1ktxJVVZcVf66FV2SJTls7QpYEucet_OV5LNudw5NzHMbj1jaOzplmsMoAUpRlJI2Bahzfh19HM35cMHTE7drHYQUwrX-GVekOBFFvQYjy_8ebJvhbc5AdplKr0hR4Um2fn6oXjVkG7oOg_ERmk3iCgV88BKXrNq5sUkHdV9HBGhEkEczAHDHLcExwgc__bWxdgKIRtCSB3503540vXbhwbVUv6l-IfJYlPObKnMP1wFW3w_yUa7n2MGCuOhv76Hfi7z2hmBzP-NpAS3PGIRNTg4pxDJxl2yc7y8JOGWOYniRBHLdpk1GE2UXwHpTRI4WORJGh75RSBk_8wdcpVshYNMTO1tJI_y0i6KcTShfmPiNjMvp5przyk1VS3v2W2V9za04bH4uwAnRfLewk5E08V0_SanmYpIXCi8rATSTbslM3iAEhfxxtAUGj1Wvq1bZYgC21hnpgaof4ZcqRQX5Sd9-yolUypaXJzw-mM8kNfGm-ZCnLM-S3x58xueODnDQ9k0yooN8M2M87KAcDjocwvr9LcRVsLJFBFa6dtSnVNi7O-P7edX5doZRbEYUTRG1Go7fKtpLSw48ZxC2A59grpeYaX0YGOinrfU8wa6MaHgiJk3LqmBz9vX1ljlbpZW5_kUxqCSnE7jbWo32OxabmfLY9oUqSKVr48jfrwDm9n3Shcgdqj4iVUOFqYkZnvw7sCxATd6kEsiw1KsxjvBcj6Y9omibav9K8t4nkLHvi7r5TJwU2-SETFjQm9_F7UGeSoxDTtDt7vpM_TS7xdg1mqiWL5KgjENgcu3s22764dzFXiM4lg_oEqYIgpuy88nLf8Zlw_o7SVFatIGM0byirzNe9t_puxXu5dbllw_hYoDyP61FtvTbZ3Da00PLr2zIqD99p7OQ2iWFL0xAvezGa8jixRkIYZQoPZzzxmSqjtL9qW79KNYwpVFOUON4gVWeZsYYqHt5NHm3WC2HV7iEv0K5HR0BXVxi1utWq3GwssTD3WzS3Znk2UtbCE_UGpMxX7DKhRjgymW_cuFXs_DEvEN8FFtMsXWpJppCpU8rBMB_Qb4lmKQDBbvtnFIrSbbZ-jy_-hhGIBxVii2-U_iZkUfzT9k5JVJD9BxaWvqRgqQfjYtv2ejSsjRTU4_3ruWLmdQ7hCQepWxtLS4HwWBtlq4iqWxy5BC60wMCZbRuvIEk6PmRIxjH-zcfDFpBm-0LrRj31r-cmhqOcWIE0JSof3pkVX-Dc0IhRhjCY0Bnr525Lg79_-eQ_yl2NYRmQfh9HSzIlvjcQf3bmGdB5P-arf7UDk0i9jPS2kiZvodxUA6QsKcYNrRilPGeEYytoslo3_N3PLNw1AbHfr9YjtIjayXG4RVNLWlTIkJZn8PcSfa-LWhAscXRgcGd1hFZta47wA0uaD_WWGhNdwLV9E-Fpzf1R5rnzKDMi2xByh1Aj--FR83w5I_HoVXIZ8YgzsR2Tshu85pApdcsTOtpIlkhdk2RbQPPvwE3BBreqoEEyNbFqcs_JafpCHI5Jvo-otkt2bKmqaVkjK8x24sACpkvT1q7OW8K68VjzM4R_5252wz77Z5QxJmvRk3I59kOJmpkz-kAOlr1UQwMpKhcMR_U2rYXwHQVVP5dQcCI8cc6Pzacp2Ekx2Jxj3dixzY2ltBaE5RHUOb5daMNS_reWcvMXpF78GDvAuOU6HR9Nlqk1qnUFed6o9luBtv-M_NNzDbkHG_m0Bb8FwswbdGO6a_oXt8tksTtdrfSv2bbKQEa13D8glYURlGrmztQaTsusQqb_zHcXmdhQEGUuCgDcePTpCvR2lfj1QH8QDo7ul8KaRAHsGWTTzrNCjryywH73D6GPuFJTOqY5doiXRtI20ecqkmPfV_SLuYaU-x0e7GTlCEc2m_rNYWIMqUUBLpTAVaIBhnrsNlx7vRmQMUFrQMjMBJKABH6GJLC7qY3j9ERQx3xUi5tPIgEHHw4BsEEk_UW30IQ9B1ap1pwTihL2gYqvMwv1E3NIpsfcj_aaDH_Csd3KWjV78TCRCXjgwkQEa2dk1LS6yPMxGrCjULCUQONTUZMoe2iKtxmnhJ-KmhGNj1pPh8xQymx8OO5gRpJJWve15eHYPKKBPfb5tHEvJr1PaT7XSYDflRhNhzTfD1DV5ElxK1Yccc47Mxq5ad7B_V_ZvH4o9yZfCWq3sdURpvi4DYI0DcVYzJD6v3GAm12EJyrpEMaPH2eIRC2YveQI2CiHXYz1AX5p-vIKWN1X87FJ7rs8sM_1Dt4MaBNNEaqMn-dDvbFvgCSHTP96pzHwUsAKZIYjyQJhudQoAeLY_bKCgH5KWKQ-wehu9E_j9iBlXwLjBQoz6S-8Sxyh-B-uV-msZ0vJoOvNteRpz5Fg58Svr9hgaQTXQ8EoCVpbN"}}
{"timestamp":"2025-09-22T13:21:24.538Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$text = [System.IO.File]::ReadAllText($path, [System.Text.Encoding]::UTF8);\\n$encoding = New-Object System.Text.UTF8Encoding($false);\\n[System.IO.File]::WriteAllText($path, $text, $encoding);\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_wMHbSIOVRhSheCDfyj9qGb9k"}}
{"timestamp":"2025-09-22T13:21:24.538Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_wMHbSIOVRhSheCDfyj9qGb9k","output":"{\"output\":\"\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:21:24.538Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:27.193Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Verifying removal of BOM change**"}}
{"timestamp":"2025-09-22T13:21:28.404Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1735640,"cached_input_tokens":1657088,"output_tokens":24096,"reasoning_output_tokens":15104,"total_tokens":1759736},"last_token_usage":{"input_tokens":53578,"cached_input_tokens":53376,"output_tokens":73,"reasoning_output_tokens":0,"total_tokens":53651},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:28.404Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Verifying removal of BOM change**"}],"content":null,"encrypted_content":"gAAAAABo0U1YTGA1u2nnhp8IxMB0oWZPmR-wAaQfSGonlndm_Q-IXH9FpUY9TS3PGe3uPSFa6AnQILpr8xPkX1aLH1mEGpj9B99KA8b_W8ClSuweOJa_Kk0vpbalrq7JWpFpMAhZbO95ALbQZFNfSQJbJSy4BeC4oRxNfidf3T-rXImECHrvmpSt4j7pSN_STfiu-ax3WD-kXheYvVv6RSTWeBDp3YIsNfTueW4uZ0tijsajv9-PNyfw6iOlneIy4LJJmAzI_U_4n-bRKtHrUBGE8HNkK2rtg1fPjXfrcMnyI0g6xdMYHrN9SD2wyi1vkWCjZRP9ZI_uG6z2jSjQCWr9wAR2rQdEoEAVa9GzdWLPY1g7HixkhvEqgjPTruH0YZJJCRVH4K0zWGfC4e-XQ0NcfPKsWno9V0XWd8AyOULeXWwckYq_Qc9vSemNllo2msATojGomzVA1yx7ElvzOwozOtMyoMzXeQ2rgolKEKVyoQZgjMbWAfLNHICNtwybvlVZEeY_rifDA0TgdGW4O0T4F_w4m9JXUTo03fV2VqLIkZpyXSj_HW03sLMMklPxLYniynSQpifrCSmqWf9Rj072Gw7BsRp1ZNW8Yqfy71P-94F1ciPBhQ9TwB4D17Ufbe5OFnNYo3A9aNBCg2ZluK_2DAK1prGk4ERDeLPQg_yxw1f98cHJpKpCFhqNsIIhvpc-hVxOZgwFVVCPWu1tFmjdzjEf8qzgy0bErrtJnEpemWCi4xqgOtKmNyxNvUCwGHl82Uv7-AALs4_smZ23752UA8U67I8gbg8jqMOgzUpuXVpwxfVOGcrcYSPMM5z5mH2gAU7hsx4rz1JowP2S4LoGsoE2ueIsP-oZCKzn8Py50q8H45XxAXDMdnxft2a4kJ6PCBVcjpDsfO_7iPjMmMOjK9feoEvd1Q=="}}
{"timestamp":"2025-09-22T13:21:28.404Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git diff NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -First 40\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_KgSRBR7SeuAuHWgLzH4O7Dx8"}}
{"timestamp":"2025-09-22T13:21:28.404Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_KgSRBR7SeuAuHWgLzH4O7Dx8","output":"{\"output\":\"diff --git a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\nindex ad3c992..d89acea 100644\\r\\n--- a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n+++ b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n@@ -121,13 +121,14 @@ namespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\r\\n             solver.SetTimeLimit(MAX_SOLVER_TIME_SECONDS * 1000);\\r\\n             \\r\\n             // 设置优化参数\\r\\n-            solver.SetSolverSpecificParametersAsString(\\r\\n-                \\\"cuts=1 \\\" +           // 启用割平�?-                \\\"heuristics=1 \\\" +     // 启用启发式算�?-                \\\"presolve=1 \\\" +       // 启用预处�?-                \\\"threads=0\\\"           // 使用所有可用线�?-            );\\r\\n-\\r\\n+            var solverParameters = string.Join(Environment.NewLine, new[]) \\r\\n+            { \\r\\n+                \\\"cuts=1\\\",            // 启用割平�?\\r\\n+                \\\"heuristics=1\\\",      // 启用启发式算�?\\r\\n+                \\\"presolve=1\\\",        // 启用预处�?\\r\\n+                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数 \\r\\n+            }); \\r\\n+            solver.SetSolverSpecificParametersAsString(solverParameters); \\r\\n             _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n             return solver;\\r\\n         }\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.4}}"}}
{"timestamp":"2025-09-22T13:21:28.404Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:41.055Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Correcting trailing spaces and formatting**"}}
{"timestamp":"2025-09-22T13:21:42.102Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1789749,"cached_input_tokens":1710592,"output_tokens":24745,"reasoning_output_tokens":15680,"total_tokens":1814494},"last_token_usage":{"input_tokens":54109,"cached_input_tokens":53504,"output_tokens":649,"reasoning_output_tokens":576,"total_tokens":54758},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:42.102Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Correcting trailing spaces and formatting**"}],"content":null,"encrypted_content":"[encoded data, 4492 characters]"}}
{"timestamp":"2025-09-22T13:21:42.102Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs -TotalCount 140\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_B9Yf5zoz4aSQNcKcTM2dSJwf"}}
{"timestamp":"2025-09-22T13:21:42.102Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_B9Yf5zoz4aSQNcKcTM2dSJwf","output":"{\"output\":\"using System;\\r\\nusing System.Collections.Generic;\\r\\nusing System.Linq;\\r\\nusing System.Threading.Tasks;\\r\\nusing Google.OrTools.LinearSolver;\\r\\nusing Microsoft.Extensions.Logging;\\r\\nusing NPP.SmartSchedue.Api.Contracts.Domain.Work;\\r\\nusing NPP.SmartSchedue.Api.Contracts.Domain.Personnel;\\r\\nusing NPP.SmartSchedue.Api.Contracts.Services.Integration.Internal;\\r\\nusing NPP.SmartSchedue.Api.Contracts.Services.Integration.Output;\\r\\n\\r\\nnamespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\r\\n{\\r\\n    /// <summary>\\r\\n    /// 增强版线性规划引�?- 实现遗传算法中的所有复杂约�?\\r\\n    /// 核心功能：三级约束体�?+ 多维度目标优�?+ 完整业务规则支持\\r\\n    /// 设计理念：将遗传算法的启发式约束转换为严格的数学约束，实现精确求�?\\r\\n    /// 技术架构：Google OR-Tools + 分层约束建模 + 自适应权重优化\\r\\n    /// 业务价值：提供数学最优解，替代遗传算法的近似解，提升调度质量\\r\\n    /// </summary>\\r\\n    public class LinearProgrammingEngine\\r\\n    {\\r\\n        private readonly ILogger<LinearProgrammingEngine> _logger;\\r\\n        private readonly ShiftRuleValidationEngine _shiftRuleEngine;\\r\\n        \\r\\n        // 求解器配�?- 增强版配置支持复杂约�?\\r\\n        private const string SOLVER_TYPE = \\\"CBC_MIXED_INTEGER_PROGRAMMING\\\";\\r\\n        private const double OPTIMALITY_TOLERANCE = 1e-6;\\r\\n        private const int MAX_SOLVER_TIME_SECONDS = 600; // 增加�?0分钟支持复杂约束\\r\\n        private const int MAX_CONSTRAINT_COUNT = 50000;   // 最大约束数量限�?\\r\\n        \\r\\n        // 约束权重配置 - 对应遗传算法的三级约束体�?\\r\\n        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 基础约束严厉惩罚\\r\\n        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 组合约束重度惩罚  \\r\\n        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 业务约束中度惩罚\\r\\n        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 公平性奖励系�?\\r\\n\\r\\n        public LinearProgrammingEngine(\\r\\n            ILogger<LinearProgrammingEngine> logger,\\r\\n            ShiftRuleValidationEngine shiftRuleEngine)\\r\\n        {\\r\\n            _logger = logger ?? throw new ArgumentNullException(nameof(logger));\\r\\n            _shiftRuleEngine = shiftRuleEngine ?? throw new ArgumentNullException(nameof(shiftRuleEngine));\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 执行增强版线性规划优�?- 实现遗传算法等价的约束体�?\\r\\n        /// 业务流程：构建决策变�?�?三级约束建模 �?多目标优�?�?精确求解 �?解析结果\\r\\n        /// 核心创新：将启发式约束转换为线性约束，实现数学最优解\\r\\n        /// </summary>\\r\\n        public async Task<GlobalOptimizedSolution> OptimizeAsync(GlobalAllocationContext context)\\r\\n        {\\r\\n            var stopwatch = System.Diagnostics.Stopwatch.StartNew();\\r\\n            \\r\\n            try\\r\\n            {\\r\\n                _logger.LogDebug(\\\"🚀 启动增强版线性规划优�?- 任务数：{TaskCount}，人员数：{PersonnelCount}，预筛选缓存：{CacheSize}\\\", \\r\\n                    context.Tasks.Count, context.AvailablePersonnel.Count, context.PrefilterResults.Count);\\r\\n\\r\\n                // 第一步：创建高性能求解�?\\r\\n                using var solver = CreateAdvancedSolver();\\r\\n\\r\\n                // 第二步：构建决策变量矩阵 (x[i,j] = 1表示任务i分配给人员j)\\r\\n                var decisionVariables = BuildDecisionVariables(solver, context);\\r\\n                _logger.LogDebug(\\\"📊 决策变量构建完成 - 变量数量：{VariableCount}\\\", decisionVariables.Count);\\r\\n\\r\\n                // 第三步：构建辅助变量 (支持复杂约束建模)\\r\\n                var auxiliaryVariables = BuildAuxiliaryVariables(solver, context, decisionVariables);\\r\\n                _logger.LogDebug(\\\"🔧 辅助变量构建完成 - 辅助变量数量：{AuxCount}\\\", auxiliaryVariables.Count);\\r\\n\\r\\n                // 第四步：Level 1 基础约束 - 对应遗传算法的基础约束验证\\r\\n                await AddLevel1BasicConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第五步：Level 2 组合约束 - 对应遗传算法的组合约束验�? \\r\\n                await AddLevel2CombinationConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第六步：Level 3 业务约束 - 对应遗传算法的业务逻辑约束\\r\\n                await AddLevel3BusinessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第七步：公平性约�?- 对应遗传算法的负载均衡机�?\\r\\n                AddFairnessConstraints(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第八步：设置多维度目标函�?- 对应遗传算法的适应度函�?\\r\\n                SetEnhancedObjectiveFunction(solver, decisionVariables, auxiliaryVariables, context);\\r\\n\\r\\n                // 第九步：执行求解\\r\\n                _logger.LogInformation(\\\"🔍 开始求解增强版线性规划模�?..\\\");\\r\\n                var resultStatus = solver.Solve();\\r\\n                \\r\\n                stopwatch.Stop();\\r\\n                \\r\\n                // 第十步：解析和验证结�?\\r\\n                var solution = await ProcessEnhancedSolverResult(solver, decisionVariables, auxiliaryVariables, \\r\\n                    context, resultStatus, stopwatch.ElapsedMilliseconds);\\r\\n                \\r\\n                return solution;\\r\\n            }\\r\\n            catch (Exception ex)\\r\\n            {\\r\\n                stopwatch.Stop();\\r\\n                _logger.LogError(ex, \\\"💥 增强版线性规划优化异常，耗时：{ElapsedMs}ms\\\", stopwatch.ElapsedMilliseconds);\\r\\n                \\r\\n                return CreateFailureSolution(ex.Message, stopwatch.ElapsedMilliseconds);\\r\\n            }\\r\\n        }\\r\\n\\r\\n        #region 求解器和变量构建\\r\\n\\r\\n        /// <summary>\\r\\n        /// 创建高性能求解�?- 支持大规模约束优�?\\r\\n        /// </summary>\\r\\n        private Solver CreateAdvancedSolver()\\r\\n        {\\r\\n            var solver = Solver.CreateSolver(SOLVER_TYPE);\\r\\n            if (solver == null)\\r\\n            {\\r\\n                throw new InvalidOperationException(\\\"无法创建增强版线性规划求解器\\\");\\r\\n            }\\r\\n\\r\\n            // 高性能配置\\r\\n            solver.SetTimeLimit(MAX_SOLVER_TIME_SECONDS * 1000);\\r\\n            \\r\\n            // 设置优化参数\\r\\n            var solverParameters = string.Join(Environment.NewLine, new[]) \\r\\n            { \\r\\n                \\\"cuts=1\\\",            // 启用割平�?\\r\\n                \\\"heuristics=1\\\",      // 启用启发式算�?\\r\\n                \\\"presolve=1\\\",        // 启用预处�?\\r\\n                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数 \\r\\n            }); \\r\\n            solver.SetSolverSpecificParametersAsString(solverParameters); \\r\\n            _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n            return solver;\\r\\n        }\\r\\n\\r\\n        /// <summary>\\r\\n        /// 【Ultra think增强】构建决策变�?- 集成人员班次不可用性的智能变量生成\\r\\n        /// 【核心优化】：在变量创建阶段就排除不可用组合，大幅提升求解效率\\r\\n        /// 【三层优化】：预筛选结�?+ 不可用性检�?+ 评分阈值，实现精确的变量裁�?\\r\\n        /// 【性能提升】：预计减少20-40%的决策变量数量，显著提升求解速度\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.4}}"}}
{"timestamp":"2025-09-22T13:21:42.102Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:21:55.846Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Cleaning and standardizing code block formatting**"}}
{"timestamp":"2025-09-22T13:21:58.625Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1846539,"cached_input_tokens":1765248,"output_tokens":25905,"reasoning_output_tokens":16448,"total_tokens":1872444},"last_token_usage":{"input_tokens":56790,"cached_input_tokens":54656,"output_tokens":1160,"reasoning_output_tokens":768,"total_tokens":57950},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:21:58.625Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Cleaning and standardizing code block formatting**"}],"content":null,"encrypted_content":"[encoded data, 5688 characters]"}}
{"timestamp":"2025-09-22T13:21:58.625Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$lines = [System.Collections.Generic.List[string]](Get-Content -Path $path);\\n$index = $lines.FindIndex({ param($line) $line.Trim() -eq 'var solverParameters = string.Join(Environment.NewLine, new[])' });\\nif ($index -lt 0) { throw 'solverParameters block not found'; }\\n$lines[$index] = '            var solverParameters = string.Join(Environment.NewLine, new[])';\\n$lines[$index + 1] = '            {';\\n$lines[$index + 2] = '                \\\"cuts=1\\\",            // 启用割平面';\\n$lines[$index + 3] = '                \\\"heuristics=1\\\",      // 启用启发式算法';\\n$lines[$index + 4] = '                \\\"presolve=1\\\",        // 启用预处理';\\n$lines[$index + 5] = '                $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // 线程数量取实际核心数';\\n$lines[$index + 6] = '            });';\\n$lines[$index + 7] = '            solver.SetSolverSpecificParametersAsString(solverParameters);';\\nif ($lines[$index + 8].Trim().Length -ne 0) {\\n    $lines.Insert($index + 8, '');\\n}\\nSet-Content -Path $path -Value $lines -Encoding utf8;\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_N4IEcRu2E6uFmYZQhVDr9bqo"}}
{"timestamp":"2025-09-22T13:21:58.625Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_N4IEcRu2E6uFmYZQhVDr9bqo","output":"{\"output\":\"\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:21:58.625Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:22:04.616Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Verifying usage of List and encoding**"}}
{"timestamp":"2025-09-22T13:22:06.010Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1904569,"cached_input_tokens":1822848,"output_tokens":26117,"reasoning_output_tokens":16576,"total_tokens":1930686},"last_token_usage":{"input_tokens":58030,"cached_input_tokens":57600,"output_tokens":212,"reasoning_output_tokens":128,"total_tokens":58242},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:22:06.010Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Verifying usage of List and encoding**"}],"content":null,"encrypted_content":"gAAAAABo0U19NhqiGpuPSWX6Ljxh8z2BLDRrkF_X9f0cEXpZZrCY2q9okrB_44O-OCET03uhfJRS-n6mMEqasKV6KaqGDbrjCT4hiqf9945OPTA9gUKfwdk_AgceAkolXuR8_MRn2epyTfjm1MLGf59vZwB81EnFA7ypezw42U79MbNGtHLYSSZXbvo8VVpNkGjDBmDdrdyXec4otrruT7tAZQoWZUT9Pt7r40DTiA19kCU0_9P2JoFIzy4vW1F1dDMpFPLsukWgIJIFaCDkIFpuCFlEdeCboTAqqm1zLIROgJgcJBQ4n6FIsFmpCEDbVo_2T7WHcNWBtCkOwgw423FwQ2lPoMVGFecF-xviVdqfmQDN9zlL0v9qPtFj_KDU7fa7DiyGz5xcyJFe4EGVwN8QF5VuGT59gaYBanOfFH0eJisDPz9h9r68D1ghKbt111fWdyBWUgwaLUB983Y2DCYixey5biVV2wrZp22luWFGuHOAzvUyYxWW3RQaogkFtlxq131vrGAzL6HgS5Zc9rSOAiHb5Z79F9dA6rZxYEVzIveqMp9ZSpDSKYGz6O-WwJlxqHUMtv1kEq5E3uiTLuEdc6S_20sP93JtYCfJs3742ehIUJjvv4v_FXfVwDTtLiTlQwpoAl-Aixn6t7l7i36WcrC730TsrefK1NVdu0Sggd1yw7YDnF_56tWXc_98MLhbgoAHK92JEPBGWHjutaUF2SfSZH34JoP4t20x2WksSRMUf7YZA2rsWNhDHkA2BvgxBnw8FAkdHPfSYg2r3UEJesyLrHH90Z8MXSLpZXTMBpyi4mq_qK5_s2HwQl1juqkRrhd1turOPDjx152fh7UNi6VKmuK2Ky6Cg2XrhMYbELlIrKtHqQHOxtQ7WYSPxTjas0p9Wa_pkL7Lxzb98ZY6p_shPetzT1YtXWffMBPrOco6kydFKgg7CnIgH62eP2DmrPBV8kzMsm_BN0d5qi5ztYL5YgDy3q5-Y5sHosnu4lQJOikigNCVwuLCVj8Ot7QfL2qIQSzrHlwK4gg7WCjRydLx19OoJ_9r6dsYTmechPcaihoUwl_XDXlYuRSo90UH2fPolmAqMcpxUsYtMfzOlylrJCdbt0LYtAkZpcmJURgRkGF3fIkUpz_Yudjquy_65WIgvZ0ADYmK9FC9UwxSToi3b_HIRJw914xj1fou8GuVtMQJcS6OZQHH5QmiwX0wRINqcNjwBs4xrG78NATutKhKe_oTBPN2Nr3J92OkOnsjOZX1LyV6y4sSTf-8RnyUE7HzlOQgi0DCCneRXKc4ADyRH34TUz6GcNSuDlp8GprI1K8CE_yhMAJgt0cysz7nLgt_Op59nWvhK_jvwKzMTOnGeFAm_KuoDQKBr6W8FXFNqLxjVAyw9X7NG6Xgbtu-s3rbRWCsEiuiOBrXw6w0xKvX9PWoOaQsc3cVHvhHMzFABnSYQXkK4jjpzVW1cLVeR1g8CQlD5NHkjGZaa9H3kozA1ENrFJi_BbC9i2DUYWpOa2bkT9g_M1z-e4MgKoprOlknOZQgCjozhcp7nQUv623gJqPzN2rO3Kcj0GzeuRdniRWyH-JlNNLJMkJJERwj7w4fc6ONthjB_DeQYqYqVzdw5vfuANMSFBn_kmZgTVo8o6uGQZGvJZfBwDcDatdb2uvGlexS"}}
{"timestamp":"2025-09-22T13:22:06.010Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"Get-Content -Path NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-String -Pattern \\\"solverParameters\\\" -Context 0,5\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_ve2v0iHmDCEzbItolZwdc9oY"}}
{"timestamp":"2025-09-22T13:22:06.010Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_ve2v0iHmDCEzbItolZwdc9oY","output":"{\"output\":\"\\r\\n>             var solverParameters = string.Join(Environment.NewLine, new[])\\r\\n              {\\r\\n                  \\\"cuts=1\\\",            // ���ø�ƽ��\\r\\n                  \\\"heuristics=1\\\",      // ��������ʽ�㷨\\r\\n                  \\\"presolve=1\\\",        // ����Ԥ����\\r\\n                  $\\\"threads={Math.Max(1, Environment.ProcessorCount)}\\\" // �߳�����ȡʵ�ʺ�����\\r\\n>             solver.SetSolverSpecificParametersAsString(solverParameters);\\r\\n  \\r\\n              _logger.LogInformation(\\\"🔧 高性能求解器创建完成，配置：时间限制{TimeLimit}s，多线程支持\\\", MAX_SOLVER_TIME_SECONDS);\\r\\n              return solver;\\r\\n          }\\r\\n  \\r\\n\\r\\n\\r\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:22:06.010Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:22:10.499Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Correcting file encoding to remove BOM**"}}
{"timestamp":"2025-09-22T13:22:11.782Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1963028,"cached_input_tokens":1880960,"output_tokens":26245,"reasoning_output_tokens":16576,"total_tokens":1989273},"last_token_usage":{"input_tokens":58459,"cached_input_tokens":58112,"output_tokens":128,"reasoning_output_tokens":0,"total_tokens":58587},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:22:11.782Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Correcting file encoding to remove BOM**"}],"content":null,"encrypted_content":"gAAAAABo0U2DTRXBZBMdzo_LNGGnhUSP7qIyvLi-69fPveQD9Lr9sXg360MItvaA14gzGbhx9d63EcDpwGsnUrAahBbto2Fv4aKW31t1E1tee-0ZpCXt4Jp6ltnMX_EjT8HSmZ0BCWTGm7b8wbfNG3V9Xn0ISgTT8beHnGhSTAmIZNRN2-fezb9g9mbSHE1FBQ_B6XvwQjObjpzKbve0TayJQy8D35we2Upp8Gz5jGn6O34gP2vRnfrRkTV8CY-3U2R1fNp1RgjBIffo9ccd5FZlN9hbggzRTTPdqN4zrSjEIG00Om6xVodXzZE5FVuPlQRJ06nfo088z1xQ2HCl97jswngJPK7fotEraGTPHZZShvip5YYQ--B2QzzeM59oUr1pcnCORTTGyU9vrvlyoBfSMqnQp8I7iiUQlm9R0-yZrLUyc4ZlGI54HHzugzOehgl6eaWk1peUSxY7vWio-raIoqyaIawECMiZAq4eIzQotjMOdi7emCynojHhJnscwzrJpIpeu05oxqs3LeH2Wn5hiT7ovaDrpl4JHn_3AcrzePHBcRW65wj0OBxH6NIqG_qHqw55jW772Yg41XuE9IZpjIlFBydVGyN-vPjmhVpbZ0KpnDEiD_vx-3VbZfcSN0g8cg83orc9lkZZai69BtZwsAC7x1kabJiSWFdvwrDq9kHz83BV7cyV7GxpqyaUVrrOmIFP5VmYza5KIg3fr6MVY8TWthCDxItZJM2PbPrFLxAqPW9CmQcsX3wm2dIePOnfPG8_3smEc4DI4bdxl5PAQGD70L22j7u7v0mi5pd9IERCSevltvT2X6fCK4cRkkXhrftLmRAtgyHWVGQwnB5ckvF0J-wHHueAOTzhe0-24ETY4YBWXDfZ6-cFcxbHwSQdhIQBYAd8y-lBwBlrIq_RdZfPYqB7GT0k9YV0NuPM2QYYS3u02MU5X_SspVLRcueNCYrRDwwnV5MDk1yc8WIfuYYshy2HsNsw65HeEwKU9jKptvEDQ7wsRokKd4BwYtI8hkQFVJCR1Ujouau3382j6rZyeCo0ab9I6HGd5rUgSh5925pbjk4Fae0nxVRpAeeVoLS1-iGvFiley5yP8ZqbRTD69WMd5sKoD5uer7rU5R6JO1IjlmWfZF5yR1vbKi1SOlOzk4H8dl_md-pifMQ5JTqKwoKiTimAjT2tr-ZSM2liq0TTR6EOWsx2rsTZ_TxXGcSIDJXLcaXhC6ayGYtw-xF7zRhNQA=="}}
{"timestamp":"2025-09-22T13:22:11.782Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"$path = 'NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs';\\n$text = [System.IO.File]::ReadAllText($path, [System.Text.Encoding]::UTF8);\\n$encoding = New-Object System.Text.UTF8Encoding($false);\\n[System.IO.File]::WriteAllText($path, $text, $encoding);\\n\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_7XkUfOUaNVW0YHS0JLxlWcG7"}}
{"timestamp":"2025-09-22T13:22:11.782Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_7XkUfOUaNVW0YHS0JLxlWcG7","output":"{\"output\":\"\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.2}}"}}
{"timestamp":"2025-09-22T13:22:11.782Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
{"timestamp":"2025-09-22T13:22:14.452Z","type":"event_msg","payload":{"type":"agent_reasoning","text":"**Reviewing code diff**"}}
{"timestamp":"2025-09-22T13:22:15.259Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2021701,"cached_input_tokens":1939456,"output_tokens":26318,"reasoning_output_tokens":16576,"total_tokens":2048019},"last_token_usage":{"input_tokens":58673,"cached_input_tokens":58496,"output_tokens":73,"reasoning_output_tokens":0,"total_tokens":58746},"model_context_window":272000}}}
{"timestamp":"2025-09-22T13:22:15.259Z","type":"response_item","payload":{"type":"reasoning","summary":[{"type":"summary_text","text":"**Reviewing code diff**"}],"content":null,"encrypted_content":"gAAAAABo0U2HyVi6N_Zki2Q2N3C5YsgKEs9n4JiA0bQtq6oIG_D2fq1ugLZzB4JTlSl-1z_zOuoNo0GVuq7JDATKSL_KXowWAyZuVQPIVO786ea4aW9-Sno3S-PWpJro34Rzz4SwEF-OpMOdc_1ohe4UtsUPq5qYshNlko4NAlbZQS0Xi9NpJIdq5vTs9LCmbOGDLwCEdpOxSXeUQaIFahuy6Fdowal_XJduGFImy5ZH-R4PWWbN217nXUKxLcQjV8ljl9beoZ9nU0koGIkQLdtWGS1fF2clXwbCEBF3cScwLUbdWEA0cfpNseippwUcEsAk5zSbASY1xkG-wBdELZK0Gfbt2HGtUf9HYiNS0hrKDFMIx72zTZycjQWQSmksP6WcDbBVLxvQ0BCPbk5fw6puTsUqB0wDB-w2bHJplCFID2UiGXBbrksggN31nOU-tQbxJ5IklEDt1jb08vmMw0BxCKXK82gIqFxddIn5gwqgdZPCvvwAs0Im3DA5ESzfCAJ3RJGq-dn7ew_4QJxM4-4sP3xlJkKVcBBrpMndxSpX8ob_wLF7sMMn6-qsaM7nriMsh_Fi0r9x49Likt55BaoWT0juJmD1Cghs4lBxo9gvnTSa-LzdB7HQSUAcrZPIRuvbM62pG-nP1OP-ky-a36er8FPnq11UcvpujoZSRyZmXlr_RBgXZjcEkCr_sunaECXoIKN92Ch1q7LInitz-TNOGnPNGgagsqkSsu4pWJ3ovxEfzX6N9PupZQpe7ZXSPA8-Ft1427LdSKxtxBDOJy7vqqndDy182FtTKc4wt-OCAkXoOome4S2F09DE4UMu4G4lnBio2F4twC2UOWfFNgOIBmPJyvAP3WQ6JfDv4asG9M39lSCyBMd-d6_TXrCm3XNQEL2BzZH2"}}
{"timestamp":"2025-09-22T13:22:15.259Z","type":"response_item","payload":{"type":"function_call","name":"shell","arguments":"{\"command\":[\"powershell.exe\",\"-NoLogo\",\"-NoProfile\",\"-Command\",\"git diff NPP.SmartSchedue.Api\\\\Services\\\\Integration\\\\Algorithms\\\\LinearProgrammingEngine.cs | Select-Object -First 40\"],\"workdir\":\"C:\\\\GitCode\\\\paiban\"}","call_id":"call_yRq4cp2yto7m0elFVIDEPBMt"}}
{"timestamp":"2025-09-22T13:22:15.260Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_yRq4cp2yto7m0elFVIDEPBMt","output":"{\"output\":\"diff --git a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\nindex ad3c992..8a4b24b 100644\\r\\n--- a/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n+++ b/NPP.SmartSchedue.Api/Services/Integration/Algorithms/LinearProgrammingEngine.cs\\r\\n@@ -12,28 +12,28 @@ using NPP.SmartSchedue.Api.Contracts.Services.Integration.Output;\\r\\n namespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\r\\n {\\r\\n     /// <summary>\\r\\n-    /// 增强版线性规划引�?- 实现遗传算法中的所有复杂约�?-    /// 核心功能：三级约束体�?+ 多维度目标优�?+ 完整业务规则支持\\r\\n-    /// 设计理念：将遗传算法的启发式约束转换为严格的数学约束，实现精确求�?-    /// 技术架构：Google OR-Tools + 分层约束建模 + 自适应权重优化\\r\\n-    /// 业务价值：提供数学最优解，替代遗传算法的近似解，提升调度质量\\r\\n+    /// 澧炲己鐗堢嚎鎬ц鍒掑紩鎿?- 瀹炵幇閬椾紶绠楁硶涓殑鎵€鏈夊鏉傜害�?\\r\\n+    /// 鏍稿績鍔熻兘锛氫笁绾х害鏉熶綋�?+ 澶氱淮搴︾洰鏍囦紭鍖?+ 瀹屾暣涓氬姟瑙勫垯鏀寔\\r\\n+    /// 璁捐鐞嗗康锛氬皢閬椾紶绠楁硶鐨勫惎鍙戝紡绾︽潫杞崲涓轰弗鏍肩殑鏁板绾︽潫锛屽疄鐜扮簿纭眰�?\\r\\n+    /// 鎶€鏈灦鏋勶細Google OR-Tools + 鍒嗗眰绾︽潫寤烘ā + 鑷€傚簲鏉冮噸浼樺�?+    /// 涓氬姟浠峰€硷細鎻愪緵鏁板鏈€浼樿В锛屾浛浠ｉ仐浼犵畻娉曠殑杩戜技瑙ｏ紝鎻愬崌璋冨害璐ㄩ噺\\r\\n     /// </summary>\\r\\n     public class LinearProgrammingEngine\\r\\n     {\\r\\n         private readonly ILogger<LinearProgrammingEngine> _logger;\\r\\n         private readonly ShiftRuleValidationEngine _shiftRuleEngine;\\r\\n         \\r\\n-        // 求解器配�?- 增强版配置支持复杂约�?+        // 姹傝В鍣ㄩ厤缃?- 澧炲己鐗堥厤缃敮鎸佸鏉傜害鏉?\\r\\n         private const string SOLVER_TYPE = \\\"CBC_MIXED_INTEGER_PROGRAMMING\\\";\\r\\n         private const double OPTIMALITY_TOLERANCE = 1e-6;\\r\\n-        private const int MAX_SOLVER_TIME_SECONDS = 600; // 增加�?0分钟支持复杂约束\\r\\n-        private const int MAX_CONSTRAINT_COUNT = 50000;   // 最大约束数量限�?+        private const int MAX_SOLVER_TIME_SECONDS = 600; // 澧炲姞鍒?0鍒嗛挓鏀寔澶嶆潅绾︽潫\\r\\n+        private const int MAX_CONSTRAINT_COUNT = 50000;   // 鏈€澶х害鏉熸暟閲忛檺�?\\r\\n         \\r\\n-        // 约束权重配置 - 对应遗传算法的三级约束体�?-        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 基础约束严厉惩罚\\r\\n-        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 组合约束重度惩罚  \\r\\n-        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 业务约束中度惩罚\\r\\n-        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 公平性奖励系�?+        // 绾︽潫鏉冮噸閰嶇�?- 瀵瑰簲閬椾紶绠楁硶鐨勪笁绾х害鏉熶綋绯?\\r\\n+        private const double LEVEL1_CONSTRAINT_PENALTY = 1000000.0; // Level 1: 鍩虹绾︽潫涓ュ帀鎯╃�?+        private const double LEVEL2_CONSTRAINT_PENALTY = 100000.0;  // Level 2: 缁勫悎绾︽潫閲嶅害鎯╃綒  \\r\\n+        private const double LEVEL3_CONSTRAINT_PENALTY = 10000.0;   // Level 3: 涓氬姟绾︽潫涓害鎯╃綒\\r\\n+        private const double FAIRNESS_REWARD_FACTOR = 1000.0;       // 鍏钩鎬у鍔辩郴�?\\r\\n \\r\\n         public LinearProgrammingEngine(\\r\\n             ILogger<LinearProgrammingEngine> logger,\\r\\n@@ -44,9 +44,9 @@ namespace NPP.SmartSchedue.Api.Services.Integration.Algorithms\\r\\n         }\\r\\n\",\"metadata\":{\"exit_code\":1,\"duration_seconds\":0.3}}"}}
{"timestamp":"2025-09-22T13:22:15.260Z","type":"turn_context","payload":{"cwd":"C:\\GitCode\\paiban","approval_policy":"never","sandbox_policy":{"mode":"danger-full-access"},"model":"gpt-5-codex","effort":"high","summary":"auto"}}
