Abstract Operations Research Modeling Using Natural Language Inputs

TL;DR

This paper presents NL2OR, an innovative pipeline leveraging large language models to automatically generate and modify operations research models from natural language inputs, reducing reliance on expert knowledge and streamlining problem formulation.

Contribution

The paper introduces NL2OR, a novel end-to-end system that uses LLMs to translate natural language queries into operational research models, enabling non-experts to formulate OR problems efficiently.

Findings

NL2OR successfully generates OR solutions from natural language inputs.

The system reduces time and expertise needed for OR problem formulation.

Experimental results demonstrate effectiveness across various OR problems.

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 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.