A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem

doi:10.1080/00207543.2022.2070786

	A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem
	Zhao, Fuqing 1; Hu, Xiaotong 1; Wang, Ling 2; Xu, Tianpeng 1; Zhu, Ningning 1; Jonrinaldi 3
刊名	International Journal of Production Research
	2022
关键词	Assembly Energy utilization Evolutionary algorithms Iterative methods Learning algorithms Multiobjective optimization Reinforcement learning Resource allocation Scheduling Brain storm optimization Clustering mechanism Energy efficient Learning mechanism No-wait flow-shop scheduling No-wait flowshop Optimisations Product assignment rule Q-learning Q-learning mechanism
ISSN号	0020-7543
DOI	10.1080/00207543.2022.2070786
英文摘要	A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. The objectives of the problem include minimising the maximum assembly completion time ((Formula presented.)), minimising the total energy consumption (TEC) and achieving resource allocation balanced. Four operations, which are critical factory insert, critical factory swap, critical factory insert to other factories, critical factory swap with other factories, are designed to optimise the objective of maximum assembly completion time. Q-learning mechanism is utilised to guide the selection of operations to avoid blind search in the iteration process. The learning mechanism based on clustering mechanism in brain storm optimisation algorithm is utilised to assign products to factories in the objective space according to the processing time of products to balance the resources allocation. The speed of operations on non-critical path is slowed down to reduce TEC regarded with the characteristics of no-wait flow shop scheduling problem. The experimental results under 810 large-scale instances by RLBSO show that the RLBSO outperforms the comparison algorithm for addressing the problem. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
语种	英语
出版者	Taylor and Francis Ltd.
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159114]
专题	国际合作处（港澳台办）计算机与通信学院科学技术处（军民融合领导小组办公室）
作者单位	1.School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou, China; 2.Department of Automation, Tsinghua University, Beijing, China; 3.Department of Industrial Engineering, Universitas Andalas, Padang, Indonesia
推荐引用方式 GB/T 7714	Zhao, Fuqing,Hu, Xiaotong,Wang, Ling,et al. A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem[J]. International Journal of Production Research,2022.
APA	Zhao, Fuqing,Hu, Xiaotong,Wang, Ling,Xu, Tianpeng,Zhu, Ningning,&Jonrinaldi.(2022).A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem.International Journal of Production Research.
MLA	Zhao, Fuqing,et al."A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem".International Journal of Production Research (2022).