面向商业优化的语言模型：生产调度中的真实世界案例研究
Language Models for Business Optimisation with a Real World Case Study in Production Scheduling
AI总结：
本文提出一个基于LLM的框架，通过微调成本高效的模型自动化商业优化中的问题建模，在生产调度和线性规划任务上生成准确建模，性能优于现有提示工程方法。
摘要：
商业优化已被广泛用于为具有挑战性的商业运营确定最优解决方案。问题建模是商业优化的重要组成部分，因为它既影响解决方案的有效性，也影响优化过程的效率。尽管已经开发了不同的优化建模语言，但问题建模仍然不是一项简单的任务，通常需要优化专业知识和问题领域知识。近年来，大型语言模型（LLMs）在不同语言相关任务中展示了卓越的性能。由于问题建模可以被视为一项翻译任务，因此有潜力利用LLMs来自动化问题建模。然而，开发用于问题建模的LLM具有挑战性，原因在于训练数据有限以及真实世界优化问题的复杂性。文献中已提出了几种提示工程方法来利用LLMs自动化问题建模。尽管初步结果令人鼓舞，但这些方法生成的建模的准确性仍有显著提升空间。在本文中，我们提出了一个基于LLM的框架，用于自动化商业优化中的问题建模。我们的方法引入了一种针对专门商业优化挑战定制微调成本高效LLM的方法。实验结果表明，我们的框架可以为生产调度中的常规和真实世界商业优化问题生成准确的建模。广泛的分析显示了所提出的微调方法的有效性和收敛性。在与文献中最先进的提示工程方法在一般线性规划问题上进行测试时，所提出的方法也表现出非常有竞争力的性能。
英文摘要：
Business optimisation has been used extensively to determine optimal solutions for challenging business operations. Problem formulation is an important part of business optimisation as it influences both the validity of solutions and the efficiency of the optimisation process. While different optimisation modelling languages have been developed, problem formulation is still not a trivial task and usually requires optimisation expertise and problem-domain knowledge. Recently, Large Language Models (LLMs) have demonstrated outstanding performance across different language-related tasks. Since problem formulation can be viewed as a translation task, there is a potential to leverage LLMs to automate problem formulation. However, developing an LLM for problem formulation is challenging, due to limited training data, and the complexity of real-world optimisation problems. Several prompt engineering methods have been proposed in the literature to automate problem formulation with LLMs. While the initial results are encouraging, the accuracy of formulations generated by these methods can still be significantly improved. In this paper, we present an LLM-based framework for automating problem formulation in business optimization. Our approach introduces a method for fine-tuning cost-efficient LLMs specifically tailored to specialized business optimization challenges. The experiment results demonstrate that our framework can generate accurate formulations for conventional and real-world business optimisation problems in production scheduling. Extensive analyses show the effectiveness and the convergence of the proposed fine-tuning method. The proposed method also shows very competitive performance when compared with the state-of-the-art prompt engineering methods in the literature when tested on general linear programming problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
