Abstract Operations Research Modeling Using Natural Language Inputs

2024-08-14

AI Summary

Operational Research (OR) modeling remains inaccessible to non-experts due to the steep learning curve of algebraic modeling languages (AMLs), which demand structured syntax and domain-specific expertise. Method: We propose the first end-to-end framework that deeply integrates large language models (LLMs) into the full OR modeling pipeline—combining LLM fine-tuning and prompt engineering, symbolic parsing, constraint validation, and seamless interfacing with commercial solvers (Gurobi/CPLEX). This enables natural-language-driven semantic understanding, automatic mathematical programming model generation, real-time correctness verification, and iterative interactive refinement. Contribution/Results: Our approach eliminates reliance on rigid input formats and prior OR knowledge. Evaluated on canonical OR tasks—including facility location, scheduling, and knapsack problems—the system achieves >85% one-shot correct model generation rate, reducing average modeling time from hours to minutes. It significantly enhances modeling efficiency, accuracy, and democratization of OR for non-specialist users.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Operational Research
• Natural Language Processing
• Scheduling Optimization

Innovation

• Natural Language Processing
• Operations Research
• Large Language Model