Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning

• URL: http://arxiv.org/abs/2508.14410v2
• Date: Fri, 22 Aug 2025 05:28:32 GMT
• Title: Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning
• Authors: Beinuo Yang, Qishen Zhou, Junyi Li, Chenxing Su, Simon Hu,
• Abstract summary: We present a novel framework that leverages expert-level optimization modeling principles through chain-of-thought reasoning to automate the optimization process.<n>We also introduce LogiOR, a new optimization modeling benchmark from the logistics domain, containing more complex problems with standardized annotations.
• Score: 43.63419208391747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization Modeling (OM) is essential for solving complex decision-making problems. However, the process remains time-consuming and error-prone, heavily relying on domain experts. While Large Language Models (LLMs) show promise in addressing these challenges through their natural language understanding and reasoning capabilities, current approaches face three critical limitations: high benchmark labeling error rates reaching up to 42%, narrow evaluation scope that only considers optimal values, and computational inefficiency due to heavy reliance on multi-agent systems or model fine-tuning. In this work, we first enhance existing datasets through systematic error correction and more comprehensive annotation. Additionally, we introduce LogiOR, a new optimization modeling benchmark from the logistics domain, containing more complex problems with standardized annotations. Furthermore, we present ORThought, a novel framework that leverages expert-level optimization modeling principles through chain-of-thought reasoning to automate the OM process. Through extensive empirical evaluation, we demonstrate that ORThought outperforms existing approaches, including multi-agent frameworks, with particularly significant advantages on complex optimization problems. Finally, we provide a systematic analysis of our method, identifying critical success factors and failure modes, providing valuable insights for future research on LLM-based optimization modeling.

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