Optimization problems are pervasive in sectors from manufacturing and
distribution to healthcare. However, most such problems are still solved
heuristically by hand rather than optimally by state-of-the-art solvers because
the expertise required to formulate and solve these problems limits the
widespread adoption of optimization tools and techniques. This paper introduces
OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and
solve (mixed integer) linear programming problems from their natural language
descriptions. OptiMUS can develop mathematical models, write and debug solver
code, evaluate the generated solutions, and improve its model and code based on
these evaluations. OptiMUS utilizes a modular structure to process problems,
allowing it to handle problems with long descriptions and complex data without
long prompts. Experiments demonstrate that OptiMUS outperforms existing
state-of-the-art methods on easy datasets by more than $20\%$ and on hard
datasets (including a new dataset, NLP4LP, released with this paper that
features long and complex problems) by more than $30\%$.

OptiMUS 是一种基于大型语言模型（LLM）的代理程序，旨在从自然语言描述中解决（混合整数）线性规划问题。OptiMUS 能够开发数学模型，编写和调试求解器代码，评估生成的解决方案，并根据这些评估改进其模型和代码。在实验证明，对于简单数据集，OptiMUS 的性能超过现有最先进方法 20％以上，对于难以处理的数据集（包括本文随附的英文描述复杂、问题较长的数据集 NLP4LP），性能超过现有最先进方法 30％以上。

OptiMUS: 可扩展的使用 (MI) LP 求解器和大型语言模型的优化建模

OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large  Language Models

We describe an augmented intelligence system for simplifying and enhancing
the modeling experience for operations research. Using this system, the user
receives a suggested formulation of an optimization problem based on its
description. To facilitate this process, we build an intuitive user interface
system that enables the users to validate and edit the suggestions. We
investigate controlled generation techniques to obtain an automatic suggestion
of formulation. Then, we evaluate their effectiveness with a newly created
dataset of linear programming problems drawn from various application domains.

介绍了一种用于优化问题建模的增强智能系统，该系统使用控制生成技术生成优化问题的自动建议，并提供用户界面进行编辑和验证，该系统经过新创建的数据集的测试表明其有效性。