Artificial intelligence for optimization: Unleashing the potential of parameter generation, model formulation, and solution methods

IF 6 2区管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

European Journal of Operational Research Pub Date : 2025-09-11 DOI:10.1016/j.ejor.2025.08.029

Zhenan Fan, Bissan Ghaddar, Xinglu Wang, Linzi Xing, Yong Zhang, Zirui Zhou

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引用次数: 0

Abstract

The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research and in particular various components of the optimization process. This survey paper explores the integration of AI with optimization (AI4OPT) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and solution methods. By providing a comprehensive overview of the state-of-the-art and examining the potential of AI to transform optimization, this paper aims to inspire further research and innovation in the development of AI-enhanced optimization methods and tools. The synergy between AI and optimization is poised to drive significant advancements and novel solutions in a multitude of domains, ultimately leading to more effective and efficient decision-making.

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优化人工智能：释放参数生成、模型制定和解决方法的潜力

人工智能（AI）技术的快速发展为变革各个领域开辟了新的机会，包括运筹学，特别是优化过程的各个组成部分。本文探讨了人工智能与优化（AI4OPT）的集成，以提高其在参数生成、模型制定和求解方法等多个阶段的有效性和效率。通过对最新技术的全面概述，并研究人工智能在优化方面的潜力，本文旨在激发人工智能增强优化方法和工具的进一步研究和创新。人工智能和优化之间的协同作用将推动众多领域的重大进步和新解决方案，最终导致更有效和高效的决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Operational Research 管理科学-运筹学与管理科学

CiteScore

11.90

自引率

9.40%

发文量

786

审稿时长

8.2 months

期刊介绍： The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.