OR for Everyone: Solving OR Problems as Non-experts with Generative AI

Jörn Maurischat (), Stephan Bogs, Grit Walther and Olaf Kirchhof
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Jörn Maurischat: Deutsche Bahn AG
Stephan Bogs: RWTH Aachen University
Grit Walther: RWTH Aachen University
Olaf Kirchhof: Deutsche Bahn AG

A chapter in Operations Research Proceedings 2024, 2025, pp 127-132 from Springer

Abstract: Abstract The potential of mathematically sophisticated OR methods is currently not being fully utilized, as their application is limited to too few contexts and requires expertise. Our work explores the potential of using Generative AI (GenAI) to enable individuals lacking expertise in Linear Programming (LP) to utilize it with minimal training effort. We conducted a small laboratory study with management consultants of Deutsche Bahn. There we assessed their ability to solve optimization problems of various complexity using ChatGPT after only a short introduction into building optimization models. We introduced half of the participants to our framework for co-programming with GenAI. The results show that participants could successfully deploy LP solutions to straightforward problems, indicating a reduction in the entry barrier. GenAI therefore creates a potential for greater accessibility and interpretability, especially under guidance. However, the efficacy of GenAI support decreased as task complexity increased, increasing the risk of undetected incorrect solutions.

Keywords: Generative AI; Linear Programming; Assisted Coding (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-92575-7_18

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DOI: 10.1007/978-3-031-92575-7_18

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