Abstract Operations Research Modeling Using Natural Language Inputs

被引：0
作者：
Li, Junxuan [1 ]
Wickman, Ryan [1 ]
Bhatnagar, Sahil [1 ]
Maity, Raj Kumar [1 ]
Mukherjee, Arko [1 ]
机构：
[1] ERP, AI ERP, One Microsoft Way, Redmond, WA 98052 USA
来源：
INFORMATION | 2025年 / 16卷 / 02期
关键词：
operations research; automated mathematical programming; large language model;
D O I：
10.3390/info16020128
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页数：16
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