Abstract Operations Research Modeling Using Natural Language Inputs
保存先:
| 出版年: | Information vol. 16, no. 2 (2025), p. 128 |
|---|---|
| 第一著者: | |
| その他の著者: | , , , |
| 出版事項: |
MDPI AG
|
| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 抄録: | 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 |
| ソース: | Advanced Technologies & Aerospace Database |