Large-scale optimization is a key backbone of modern business decision-making. However, building these models is often labor-intensive and time-consuming. We address this by proposing LEAN-LLM-OPT, a LightwEight AgeNtic workflow construction framework for LLM-assisted large-scale OPTimization auto-formulation. LEAN-LLM-OPT takes as input a problem description together with associated datasets and orchestrates a team of LLM agents to produce an optimization formulation. Specifically, upon receiving a query, two upstream LLM agents dynamically construct a workflow that specifies, step-by-step, how optimization models for similar problems can be formulated. A downstream LLM agent then follows this workflow to generate the final output. Leveraging LLMs' text-processing capabilities and common modeling practices, the workflow decomposes the modeling task into a sequence of structured sub-tasks and offloads mechanical data-handling operations to auxiliary tools. This design alleviates the downstream agent's burden related to planning and data handling, allowing it to focus on the most challenging components that cannot be readily standardized. Extensive simulations show that LEAN-LLM-OPT, instantiated with GPT-4.1 and the open source gpt-oss-20B, achieves strong performance on large-scale optimization modeling tasks and is competitive with state-of-the-art approaches. In addition, in a Singapore Airlines choice-based revenue management use case, LEAN-LLM-OPT demonstrates practical value by achieving leading performance across a range of scenarios. Along the way, we introduce Large-Scale-OR and Air-NRM, the first comprehensive benchmarks for large-scale optimization auto-formulation. The code and data of this work is available at https://github.com/CoraLiang01/lean-llm-opt.

翻译：大规模优化是现代商业决策的关键支柱。然而，构建此类模型通常需要大量人力且耗时。为此，我们提出LEAN-LLM-OPT，一种用于LLM辅助大规模优化自动构建的轻量级智能工作流构造框架。LEAN-LLM-OPT以问题描述及相关数据集作为输入，通过协调一组LLM智能体来生成优化模型公式。具体而言，在接收到查询后，两个上游LLM智能体动态构建一个工作流，该工作流逐步指定了如何为类似问题构建优化模型。随后，一个下游LLM智能体遵循此工作流生成最终输出。该框架利用LLM的文本处理能力和通用建模实践，将建模任务分解为一系列结构化的子任务，并将机械化的数据处理操作卸载给辅助工具。这种设计减轻了下游智能体在规划与数据处理方面的负担，使其能够专注于那些难以标准化的最具挑战性的组件。大量仿真实验表明，基于GPT-4.1和开源模型gpt-oss-20B实例化的LEAN-LLM-OPT在大规模优化建模任务上表现出色，并与最先进方法具有竞争力。此外，在新加坡航空公司基于选择的收益管理应用案例中，LEAN-LLM-OPT在一系列场景中均取得领先性能，展示了其实用价值。在此过程中，我们引入了Large-Scale-OR和Air-NRM，这是首个面向大规模优化自动构建的综合基准测试集。本工作的代码与数据可在 https://github.com/CoraLiang01/lean-llm-opt 获取。