做供应链排程或排课系统的团队终于有了不用求OR专家的方案——LLM直接帮你改模型、选算法、出解，建议做运筹优化的开发者点开看实现细节。
本文提出一个基于LLM的智能体重优化框架，让非运筹学专家也能通过自然语言交互快速调整优化模型。该框架将LLM作为运筹学专家，将用户提示转化为结构化模型更新，并从优化工具箱中选择合适技术加速重优化。工具箱利用历史解、有效不等式、求解器配置和元启发式等原始信息，在保证解质量的同时提升计算效率。在两个大规模真实案例（在线供应链重优化和离线大学考试排程）中验证了其有效性和可扩展性。该框架减少了对运筹学专家的依赖，提升了决策支持系统的可持续性。
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynamic, with evolving business rules, previously overlooked constraints, and unforeseen perturbations. In such contexts, end users must rapidly re-optimize models to recover feasible and implementable solutions. This paper introduces an agentic re-optimization framework in which a large language model (LLM) acts as an OR expert, dynamically supporting end users through natural-language interaction. The LLM translates user prompts into structured updates of the underlying optimization model, selects suitable re-optimization techniques from an optimization toolbox, and solves the resulting instance to return implementable solutions. The toolbox leverages primal information, including historical solutions, valid inequalities, solver configurations, and metaheuristics, to accelerate re-optimization while preserving solution quality. The proposed framework enables interactive and continuous adaptation of deployed optimization models, reducing dependence on OR experts and improving the sustainability of decision-support systems. Extensive experiments on two complementary large-scale real-world case studies demonstrate the effectiveness and scalability of the proposed framework. The first considers online supply chain re-optimization, where solutions must be generated rapidly while remaining close to the deployed plan, whereas the second focuses on offline university exam scheduling, where solution quality is prioritized over runtime. Results show that the toolbox-driven architecture significantly improves computational efficiency through primal-based and solver-aware re-optimization techniques, while the structured patch-based updates improve interpretability and traceability of model modifications.