# Large Language Models for Operations Research: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2605.20849 · HTML: https://mlsanity.com/paper/2605.20849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2026.

## Built on by (most cited first, 2 of 2)

- [Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing](https://mlsanity.com/paper/2606.31820/) (2026)
- [Robust Feasible Route Construction through Collaborative Partition Optimization](https://mlsanity.com/paper/2607.03694/) (2026)

## Abstract (arXiv)

Operations Research (OR) serves as a core decision-support methodology for complex systems, with significant applications across mathematics, management science, and computer science. Traditional approaches heavily rely on expert knowledge and often struggle to efficiently solve large-scale and multi-constraint problems. The rapid advancement of Large Language Models (LLMs) in recent years has offered a novel research paradigm to address these challenges. This paper presents a systematic survey of Large Language Models for Operations Research (LLM4OR). We begin by introducing the definition of OR problems and the fundamental principles of LLMs. We then focus on analyzing the roles of LLMs in OR, specifically covering such as model formulation, algorithm design, and solution verification. In addition, we discuss practical applications in representative scenarios and summarize benchmark datasets in this field. Finally, we outline the key challenges and provide perspectives on future research directions. A collection of related literature is available at https://github.com/xianchaoxiu/LLM4OR.