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

Xiao, Ziyang; Xie, Jingrong; Xu, Lilin; Guan, Shisi; Zhu, Jingyan; Han, Xiongwei; Fu, Xiaojin; Yu, WingYin; Wu, Han; Shi, Wei; Kang, Qingcan; Duan, Jiahui; Zhong, Tao; Yuan, Mingxuan; Zeng, Jia; Wang, Yuan; Chen, Gang; Zhang, Dongxiang

doi:10.24963/ijcai.2025/1192

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

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

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10742-10750.

https://doi.org/10.24963/ijcai.2025/1192

Keywords

Natural Language Processing: NLP: Applications

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.

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, and Dongxiang Zhang. 2025. A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10742–10750. https://doi.org/10.24963/ijcai.2025/1192

@inproceedings{ijcai2025p1192,
  title     = {A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions},
  author    = {Xiao, Ziyang and Xie, Jingrong and Xu, Lilin and Guan, Shisi and Zhu, Jingyan and Han, Xiongwei and Fu, Xiaojin and Yu, WingYin and Wu, Han and Shi, Wei and Kang, Qingcan and Duan, Jiahui and Zhong, Tao and Yuan, Mingxuan and Zeng, Jia and Wang, Yuan and Chen, Gang and Zhang, Dongxiang},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10742--10750},
  year      = {2025},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2025/1192},
  url       = {https://doi.org/10.24963/ijcai.2025/1192},
}

TY  - CPAPER
TI  - A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
AU  - Xiao, Ziyang
AU  - Xie, Jingrong
AU  - Xu, Lilin
AU  - Guan, Shisi
AU  - Zhu, Jingyan
AU  - Han, Xiongwei
AU  - Fu, Xiaojin
AU  - Yu, WingYin
AU  - Wu, Han
AU  - Shi, Wei
AU  - Kang, Qingcan
AU  - Duan, Jiahui
AU  - Zhong, Tao
AU  - Yuan, Mingxuan
AU  - Zeng, Jia
AU  - Wang, Yuan
AU  - Chen, Gang
AU  - Zhang, Dongxiang
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10742
EP  - 10750
DO  - 10.24963/ijcai.2025/1192
UR  - https://doi.org/10.24963/ijcai.2025/1192
ER  -