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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.10047 |
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| _version_ | 1866911105276706816 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Artificial Intelligence 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. |
| title | A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.10047 |