强化学习方法应用于作业车间调度问题的文献综述

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Operations Research Pub Date : 2024-11-27 DOI:10.1016/j.cor.2024.106929
Xiehui Zhang, Guang-Yu Zhu
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引用次数: 0

摘要

车间作业调度问题(JSP)是最著名的生产调度问题之一,属于np困难问题。强化学习(RL)是一种基于反馈学习的机器学习方法,在解决车间调度问题方面具有很大的潜力。本文以应用强化学习解决jsp的文献为综述对象,从强化学习方法、agent数量、agent升级策略等方面进行分析。我们讨论了RL方法在解决jsp时面临的三个主要问题:维度的诅咒、泛化性和训练时间。揭示了三个主要问题的相互联系,并确定了影响它们的主要因素。通过讨论目前解决上述问题的方法以及存在的其他挑战,提出了解决这些问题的建议,并提出了未来的研究趋势。
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A literature review of reinforcement learning methods applied to job-shop scheduling problems
The job-shop scheduling problem (JSP) is one of the most famous production scheduling problems, and it is an NP-hard problem. Reinforcement learning (RL), a machine learning method capable of feedback-based learning, holds great potential for solving shop scheduling problems. In this paper, the literature on applying RL to solve JSPs is taken as the review object and analyzed in terms of RL methods, the number of agents, and the agent upgrade strategy. We discuss three major issues faced by RL methods for solving JSPs: the curse of dimensionality, the generalizability and the training time. The interconnectedness of the three main issues is revealed and the main factors affecting them are identified. By discussing the current solutions to the above issues as well as other challenges that exist, suggestions for solving these problems are given, and future research trends are proposed.
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来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
期刊最新文献
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