Abstract Operations Research Modeling Using Natural Language Inputs
This addresses the challenge for non-experts in operations research by enabling easier and faster model creation, though it appears incremental as it builds on existing automated mathematical programming with LLM advancements.
The paper tackles the problem of automating operations research modeling, which typically requires expert knowledge and is time-consuming, by introducing a methodology that uses Large Language Models to create and edit solutions from natural language inputs, reducing the need for expertise and formulation time.
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.