基于q学习的多目标斑点鬣狗算法用于考虑预防性维护和行程/设置时间的灵活开放车间调度问题

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems Pub Date : 2025-06-16 DOI:10.1016/j.jmsy.2025.06.001

Jun Guo , Bin Peng , Baigang Du , Kaipu Wang , Yibing Li

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引用次数: 0

摘要

本文提出了一个考虑预防性维修、机器之间的行程时间和顺序相关的设置时间（fossp - pm&； TT）的灵活开放车间调度问题，以解决日常维修对车间生产率的影响。根据该问题的特点，建立了同时最小化最大完工时间和平均流程时间的数学模型。然后，提出一种基于q学习的斑点鬣狗多目标优化算法（Q-MSHO）来解决这一问题。根据fosp - pm&；TT的特点，设计了四个邻域结构。提出了一种基于q学习的变量邻域搜索策略，以更新每次迭代中局部搜索操作的选择。最后，在不同大小的测试实例上进行了计算实验，以评估所提算法的性能。实验结果表明，与其他算法相比，Q-MSHO算法在解决fosp - pm&；TT问题方面表现出优越的性能。

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Q-learning-based multi-objective spotted hyena algorithm for flexible open shop scheduling problem with consideration of preventive maintenance and travel/setup times

This paper presents a flexible open shop scheduling problem considering preventive maintenance, travel time between machines, and sequence-dependent setup time (FOSSP-PM&TT) to address the impact of routine maintenance on shop productivity. According to the characteristics of the problem, a mathematical model is developed to simultaneously minimize the makespan and mean flow time. Then, a Q-learning-based multi-objective spotted hyena optimization algorithm (Q-MSHO) is proposed to solve this problem. Four neighborhood structures are designed in accordance with characteristics of the FOSSP-PM&TT. And a Q-learning-based variable neighborhood search strategy is proposed to update the selection of local search operations in each iteration. Finally, computational experiments are performed on test instances of different sizes to evaluate the performance of the proposed algorithm. The experimental outcomes demonstrate that the Q-MSHO algorithm exhibits superior performance compared to the other algorithms in addressing the FOSSP-PM&TT problem.

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来源期刊

Journal of Manufacturing Systems 工程技术-工程：工业

CiteScore

23.30

自引率

13.20%

发文量

216

审稿时长

25 days

期刊介绍： The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.