题　目：LLM for Large-Scale Optimization Model Auto-Formulation: A Few-Shot Learning Approach

嘉　宾：覃含章 助理教授 新加坡国立大学

主持人：许欢 教授 上海交通大学安泰经济与管理学院

时　间：2025年5月21日（周三）14:00-15:30

地　点：安泰楼A507室

内容简介：

As modern business problems grow increasingly complex, large-scale optimization modeling becomes a critical backbone of decision-making. However, traditional optimization modeling is often labor-intensive, which can be both costly and time-consuming. We address this by proposing LEAN-LLM-OPT, a novel LightwEight few-shot leArNing framework for LLM-guided large-scale OPTimization model auto-formulation, that takes a query (a problem description and associated datasets) as input and orchestrates a team of LLM agents to output the optimization formulation. LEAN-LLM-OPT innovatively uses few-shot learning to demonstrate how to use customized retrieval-augmented generation (RAG) tools to enhance LLM agents in optimization modeling. Specifically, upon receiving a query, a problem classification agent first determines the type of the problem. Then, a few-shot example generation agent consolidates a set of references to demonstrate how optimization models are built for problems of same type. Finally, a model generation agent learns from these examples to extract relevant information from the input datasets using RAG tools and generate the final optimization model. Extensive simulations demonstrate that LEAN-LLM-OPT achieves state-of-the-art modeling accuracy compared to existing methods, especially on large-scale optimization modeling. Additionally, we validate its effectiveness through a Singapore Airlines flight scheduling use case. As another practical contribution, we introduce Large-Scale-OR and Air-NRM, the first set of large-scale optimization model formulation benchmarks based on real-world applications across different domains. Our results thus provide a resource-efficient solution for large-scale optimization model auto-formulation and offers evidences for LLMs as few-shot learners in this regime. A demo of LEAN-LLM-OPT is available at https://lean-opt-llm.streamlit.app.

演讲人简介：

Hanzhang Qin is an Assistant Professor at the Department of Industrial Systems Engineering and Management at NUS. He is also an affiliated faculty member at the NUS Institute for Operations Research and Analytics and the NUS AI Institute. His research was recognized by several awards, including INFORMS TSL Intelligent Transportation Systems Best Paper Award and MIT MathWorks Prize for Outstanding CSE Doctoral Research. Before joining NUS, Hanzhang spent one year as a postdoctoral scientist in the Supply Chain Optimization Technologies Group of Amazon NYC. He earned his PhD in Computational Science and Engineering under supervision of Professor David Simchi-Levi, and his research interests span stochastic control, applied probability and statistical learning, with applications in supply chain analytics and transportation systems. He holds two master's, one in EECS and one in Transportation both from MIT. Prior to attending MIT, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University.

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