将真实业务问题自动转为可求解优化模型，支持生产环境复杂数据。
ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling
- 通过对话、数据采集等四组件协同，处理模糊描述与原始数据。
- 在工业级数据集上准确率超现有工具，支持多求解器无缝切换。
- 适合企业落地部署，解决真实业务中从需求到模型的断层问题。
本文提出ORPilot，一个面向生产环境的开源智能体式大模型优化建模系统，能将真实业务问题转化为求解器可用的优化模型。与学术界假设清晰输入的LLM-for-OR工具不同，ORPilot专为生产场景设计：应对模糊描述、大规模原始操作数据，并支持跨求解器移植。系统引入四项创新：（1）对话式访谈代理以获取完整问题规格；（2）独立于提示的数据采集代理；（3）参数计算代理，将原始表格数据转换为模型可用参数；（4）求解器无关的中间表示（IR），实现确定性零大模型调用重编译至Gurobi、CPLEX、PuLP、Pyomo或OR-Tools。自纠错重试循环利用求解器回溯信息进行定向修复。这是首个针对生产级业务问题而非教科书案例的尝试。在真实问题上的评估显示，相较于传统学术基准（IndustryOR、NL4OPT、NLP4LP），ORPilot在IndustryOR上准确率优于当前最优工具，且在其他两个基准上表现相当。
原文摘要 · Abstract (English)
This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike academic LLM-for-OR tools that assume clean problem specifications with preformatted inline data, ORPilot is designed for production conditions: ambiguous descriptions, large-scale raw operational data, and the need for portability across solver backends. The system introduces four novel components: (1) a conversational interview agent to elicit complete problem specifications, (2) a data collection agent that retrieves data independently of prompts, (3) a parameter computation agent to bridge raw tabular data and model-ready parameters, and (4) a solver-agnostic Intermediate Representation (IR) for deterministic, zero-LLM-call recompilation to Gurobi, CPLEX, PuLP, Pyomo, or OR-Tools solvers. Additionally, self-correcting retry loops utilize solver tracebacks for targeted repairs. ORPilot represents the first attempt to target production-level business problems rather than textbook operations research (OR) cases. Evaluation on real-world problems demonstrates promising results. When tested against traditional academic benchmarks: IndustryOR, NL4OPT and NLP4LP, ORPilot outperformed state-of-the-art tools in accuracy on the IndustryOR benchmark and delivered comparable performance on NL4OPT and NLP4LP.
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