组织者
Organizer
Yuan Zhou 周源 (YMSC)
报告人
Speaker
Hanzhang Qin 覃含章 (NUS)
时间
Time
Thur., 15:30-17:00, May 28, 2026
地点
Venue
C654, Shuangqing Complex Building A
Large-Scale Optimization Model Auto-Formulation: Harnessing LLM Flexibility via Structured Workflow
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 structure the modeling process into a sequence of 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.
About the speaker
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, APORS Young Researcher 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.
