Large Language Models for Operations Research: A Comprehensive Survey

TL;DR

This survey explores how Large Language Models are transforming Operations Research by improving problem formulation, algorithm design, and solution verification, with practical applications and future challenges discussed.

Contribution

It provides a comprehensive overview of LLM applications in OR, highlighting recent advances, benchmark datasets, and future research directions.

Findings

LLMs assist in model formulation and solution verification in OR.

Practical applications demonstrate LLMs' effectiveness in complex decision-making.

Benchmark datasets facilitate standardized evaluation of LLMs in OR.

Abstract

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.