A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions

• URL: http://arxiv.org/abs/2508.10047v1
• Date: Tue, 12 Aug 2025 06:55:33 GMT
• Title: A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
• Authors: Ziyang Xiao, Jingrong Xie, Lilin Xu, Shisi Guan, Jingyan Zhu, Xiongwei Han, Xiaojin Fu, WingYin Yu, Han Wu, Wei Shi, Qingcan Kang, Jiahui Duan, Tao Zhong, Mingxuan Yuan, Jia Zeng, Yuan Wang, Gang Chen, Dongxiang Zhang,
• Abstract summary: With the advent of large language models (LLMs), new opportunities have emerged to automate the procedure of mathematical modeling.<n>This survey presents a comprehensive review of recent advancements that cover the entire technical stack.
• Score: 27.77977859998504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires substantial expertise from operations research professionals. With the advent of large language models (LLMs), new opportunities have emerged to automate the procedure of mathematical modeling. This survey presents a comprehensive and timely review of recent advancements that cover the entire technical stack, including data synthesis and fine-tuning for the base model, inference frameworks, benchmark datasets, and performance evaluation. In addition, we conducted an in-depth analysis on the quality of benchmark datasets, which was found to have a surprisingly high error rate. We cleaned the datasets and constructed a new leaderboard with fair performance evaluation in terms of base LLM model and datasets. We also build an online portal that integrates resources of cleaned datasets, code and paper repository to benefit the community. Finally, we identify limitations in current methodologies and outline future research opportunities.

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