Two-stage learning scatter search algorithm for the distributed hybrid flow shop scheduling problem with machine breakdown

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Expert Systems with Applications Pub Date : 2024-09-13 DOI:10.1016/j.eswa.2024.125344

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Abstract

The distributed hybrid flow shop scheduling problem with machine breakdown is investigated to reduce the negative impact on real production caused by machine breakdown events. (DHFSPMB). DHFSPMB comprises two subproblems: the maintenance problem with machine breakdown and the distributed hybrid flow shop scheduling problem (DHFSP). A rescheduling method is designed to address the maintenance problem. Subsequently, a two-stage learning scatter search (TLSS) algorithm is proposed for optimizing the DHFSP when the machines break down. Firstly, a mixed integer programming model for DHFSPMB is constructed. Secondly, TLSS employs an improved reinforcement learning approach to enhance the capability of exploration by guiding the direction of global search. A two-stage approach is designed to address the lack of knowledge in the early periods of learning. Finally, a hybrid search strategy is devised to enhance the development capability of TLSS. The experimental results demonstrate that the TLSS algorithm outperforms the comparison algorithms in effectively addressing the DHFSPMB.

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带机器故障的分布式混合流程车间调度问题的两阶段学习散点搜索算法

研究了带有机器故障的分布式混合流程车间调度问题，以减少机器故障事件对实际生产造成的负面影响。(DHFSPMB）。DHFSPMB 包括两个子问题：带机器故障的维护问题和分布式混合流程车间调度问题 (DHFSP)。设计了一种重新调度方法来解决维护问题。随后，提出了一种两阶段学习散点搜索（TLSS）算法，用于优化机器故障时的 DHFSP。首先，构建了 DHFSPMB 的混合整数编程模型。其次，TLSS 采用改进的强化学习方法，通过引导全局搜索方向来增强探索能力。设计了一种两阶段方法来解决学习初期的知识匮乏问题。最后，设计了一种混合搜索策略，以增强 TLSS 的开发能力。实验结果表明，TLSS 算法在有效处理 DHFSPMB 方面优于对比算法。

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来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.