Large Language Models and Operations Research: A Structured Survey

• URL: http://arxiv.org/abs/2509.18180v1
• Date: Thu, 18 Sep 2025 01:52:19 GMT
• Title: Large Language Models and Operations Research: A Structured Survey
• Authors: Yang Wang, Kai Li,
• Abstract summary: Large language models (LLMs) have shown potential to address limitations through semantic understanding, structured generation, and reasoning control.<n>LLMs can translate natural language descriptions into mathematical models or executable code, generate benchmarks, evolve algorithms, and tackle optimization tasks.
• Score: 9.208082097215314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Operations research (OR) provides fundamental methodologies for complex system decision-making, with established applications in transportation, supply chain management, and production scheduling. Traditional approaches, which depend on expert-based modeling and manual parameter adjustment, often face challenges in handling large-scale, dynamic, and multi-constraint problems. Recently, large language models (LLMs) have shown potential to address these limitations through semantic understanding, structured generation, and reasoning control. LLMs can translate natural language descriptions into mathematical models or executable code, generate heuristics, evolve algorithms, and directly tackle optimization tasks. This paper surveys recent progress on the integration of LLMs into OR, organizing methods into three main directions: automatic modeling, auxiliary optimization, and direct solving. It further reviews evaluation benchmarks and domain-specific applications, and summarizes key open issues such as unstable semantic-to-structure mapping, fragmented research progress, limited generalization, and insufficient evaluation systems. Finally, the survey outlines possible research avenues for advancing the role of LLMs in OR.

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