Learning to guide local search optimisation for routing problems

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-07-01 DOI:10.1016/j.orl.2024.107136

Nasrin Sultana , Jeffrey Chan , Babak Abbasi , Tabinda Sarwar , A.K. Qin

引用次数: 0

Abstract

Machine learning has shown promises in tackling routing problems yet falls short of state-of-the-art solutions achieved by stand-alone operations research algorithms. This paper introduces “Learning to Guide Local Search” (L2GLS), a novel approach that leverages Local Search operators' strengths and reinforcement learning to adjust search efforts adaptively. The results of comparing L2GLS with the existing cutting-edge approaches indicate that L2GLS attains new levels of state-of-the-art performance, particularly excelling in handling large instances that continue to challenge existing algorithms.

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学习引导路由问题的本地搜索优化

机器学习在解决路由问题方面大有可为，但与独立运筹学算法实现的最先进解决方案相比仍有差距。本文介绍了 "学习引导本地搜索"（L2GLS），这是一种利用本地搜索算子的优势和强化学习来自适应调整搜索工作的新方法。将 L2GLS 与现有先进方法进行比较的结果表明，L2GLS 的性能达到了最先进的新水平，尤其是在处理大型实例方面表现出色，而这些实例一直是对现有算法的挑战。

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

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.