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The use of Generative Artificial Intelligence (GenAI) in operations research: review and future research agenda

The use of Generative Artificial Intelligence (GenAI) in operations research: review and future research agenda
The use of Generative Artificial Intelligence (GenAI) in operations research: review and future research agenda

The emergence of Generative Artificial Intelligence (GenAI) represents a significant advancement in computational capabilities, offering transformative potential for the field of Operations Research (OR). This study explores the role of GenAI in OR by conducting a systematic literature review. Following a careful analysis of the collected works, the reviewed papers are classified into two main categories based on the nature of their contributions: (1) application papers and (2) review and position papers. The latter provide a conceptual overview of GenAI’s broader implications for OR, while the application papers are organized into a taxonomy encompassing three core dimensions: (1) GenAI for mathematical programming and optimization, (2) GenAI for stochastic systems, and (3) GenAI for simulation, strategic analysis, game theory, and risk management. Drawing insights from both conceptual and empirical studies, this review identifies cross-cutting themes and outlines a future research agenda to guide continued exploration at the intersection of GenAI and OR.

Generative AI, mathematical programming, operations research, optimization, simulation, stochastic systems
0160-5682
Zhou, Qin
22cc3c1b-50f4-41e0-9c3e-8cdf183a022e
Sheu, Jiuh-Biing
89b339c4-1855-4424-8b52-ed279c7030cc
Zhou, Qin
22cc3c1b-50f4-41e0-9c3e-8cdf183a022e
Sheu, Jiuh-Biing
89b339c4-1855-4424-8b52-ed279c7030cc

Zhou, Qin and Sheu, Jiuh-Biing (2025) The use of Generative Artificial Intelligence (GenAI) in operations research: review and future research agenda. Journal of the Operational Research Society. (doi:10.1080/01605682.2025.2561762).

Record type: Review

Abstract

The emergence of Generative Artificial Intelligence (GenAI) represents a significant advancement in computational capabilities, offering transformative potential for the field of Operations Research (OR). This study explores the role of GenAI in OR by conducting a systematic literature review. Following a careful analysis of the collected works, the reviewed papers are classified into two main categories based on the nature of their contributions: (1) application papers and (2) review and position papers. The latter provide a conceptual overview of GenAI’s broader implications for OR, while the application papers are organized into a taxonomy encompassing three core dimensions: (1) GenAI for mathematical programming and optimization, (2) GenAI for stochastic systems, and (3) GenAI for simulation, strategic analysis, game theory, and risk management. Drawing insights from both conceptual and empirical studies, this review identifies cross-cutting themes and outlines a future research agenda to guide continued exploration at the intersection of GenAI and OR.

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Accepted/In Press date: 12 September 2025
e-pub ahead of print date: 17 September 2025
Keywords: Generative AI, mathematical programming, operations research, optimization, simulation, stochastic systems

Identifiers

Local EPrints ID: 506236
URI: http://eprints.soton.ac.uk/id/eprint/506236
ISSN: 0160-5682
PURE UUID: 48f18bc8-89ae-4ab8-adb8-59e5925e7bd6
ORCID for Qin Zhou: ORCID iD orcid.org/0000-0002-0273-6295

Catalogue record

Date deposited: 30 Oct 2025 17:51
Last modified: 31 Oct 2025 03:03

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Contributors

Author: Qin Zhou ORCID iD
Author: Jiuh-Biing Sheu

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