Year: 2025
Type: conference-abstract
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.
Source: Information
DOI: 10.3390/info16020128
Institution: Microsoft (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4407340340