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. The agentic workflow leverages common modeling practices to standardize the modeling process into a sequence of structured sub-tasks, offloading mechanical data-handling operations to auxiliary tools. This reduces the LLM's burden in planning and data handling, allowing us to exploit its flexibility to address unstructured components. 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密集的人工投入且耗时良久。为此，我们提出LEAN-LLM-OPT，一个用于大语言模型辅助的大规模优化自动构建的轻量级智能体工作流框架。LEAN-LLM-OPT以问题描述及相关数据集作为输入，并协调一组大语言模型智能体以生成优化模型公式。具体而言，在接收到查询后，两个上游大语言模型智能体会动态构建一个工作流，该工作流逐步规定了如何为类似问题构建优化模型。随后，一个下游大语言模型智能体遵循此工作流生成最终输出。该智能体工作流利用常见的建模实践，将建模过程标准化为一系列结构化的子任务，并将机械的数据处理操作卸载给辅助工具。这减轻了大语言模型在规划和数据处理上的负担，使我们能够利用其灵活性来处理非结构化部分。大量模拟实验表明，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取得了强劲的性能，并与最先进方法相比具有竞争力。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在一系列场景中实现了领先性能，展示了其实用价值。在此过程中，我们引入了Large-Scale-OR和Air-NRM，这是首个用于大规模优化自动构建的综合基准。本工作的代码和数据可在 https://github.com/CoraLiang01/lean-llm-opt 获取。