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 Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The 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.
@article{arxiv.2408.07272,
title = {Abstract Operations Research Modeling Using Natural Language Inputs},
author = {Junxuan Li and Ryan Wickman and Sahil Bhatnagar and Raj Kumar Maity and Arko Mukherjee},
journal= {arXiv preprint arXiv:2408.07272},
year = {2025}
}