Abstract Operations Research Modeling Using Natural Language Inputs

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Pubblicato in:	Information vol. 16, no. 2 (2025), p. 128
Autore principale:	Li, Junxuan
Altri autori:	Wickman, Ryan, Bhatnagar, Sahil, Maity, Raj Kumar, Mukherjee, Arko
Pubblicazione:	MDPI AG
Soggetti:	Mathematical programming Problem solving Language Scheduling Integer programming User needs Large language models Concrete Optimization Decision making Natural language processing Automation Queries Order processing
Accesso online:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descrizione
Abstract:	Operations research (OR) uses mathematical models to enhance decision making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in a large language model (LLM) to create and edit abstract OR models from non-expert user queries expressed using natural language. This reduces the need for domain expertise and the time to formulate a problem, and an abstract OR model generated can be deployed to a multi-tenant platform to support a class of users with different input data. This paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems.
ISSN:	2078-2489
DOI:	10.3390/info16020128
Fonte:	Advanced Technologies & Aerospace Database