A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem

doi:10.1109/TII.2022.3192881

	A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem
	Zhao, Fuqing 1; Xu, Zesong 1; Wang, Ling 3; Zhu, Ningning 1; Xu, Tianpeng 1; Jonrinaldi 2
刊名	IEEE Transactions on Industrial Informatics
	2022
页码	1-12
关键词	Benchmarking Integer programming Job shop scheduling Local search (optimization) Machine shop practice Manufacture Population statistics Scheduling algorithms Assembly scheduling Distributed assembly scheduling Greedy algorithms Iterated greedy algorithm Job-Shop scheduling No-wait flowshop Processor scheduling Production facility Search problem Total flowtime
ISSN号	1551-3203
DOI	10.1109/TII.2022.3192881
英文摘要	This paper investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed. A population-based iterated greedy algorithm (PBIGA) is presented to address the problem. A new constructive heuristic is presented to generate an initial population with high quality. For DANWFSP, an accelerated NR3 algorithm is proposed to assign jobs to the factories, which improves the efficiency of the algorithm and saves CPU time. To enhance the effectiveness of the PBIGA, the local search method and the destruction-construction mechanisms are designed for the product sequence and job sequence, respectively. A selection mechanism is presented to determine which individuals execute the local search method. An acceptance criterion is proposed to determine whether the offspring are adopted by the population. Last, the PBIGA and seven state-of-the-art algorithms are tested on 810 large-scale benchmark instances. The experimental results show that the presented PBIGA is an effective algorithm to address the problem and performs better than recently state-of-the-art algorithms compared in this paper. IEEE
语种	英语
出版者	IEEE Computer Society
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159776]
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 Industrial Engineering, Universitas Andalas, Padang, Indonesia 3.Department of Automation, Tsinghua University, Beijing, China;
推荐引用方式 GB/T 7714	Zhao, Fuqing,Xu, Zesong,Wang, Ling,et al. A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem[J]. IEEE Transactions on Industrial Informatics,2022:1-12.
APA	Zhao, Fuqing,Xu, Zesong,Wang, Ling,Zhu, Ningning,Xu, Tianpeng,&Jonrinaldi.(2022).A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem.IEEE Transactions on Industrial Informatics,1-12.
MLA	Zhao, Fuqing,et al."A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem".IEEE Transactions on Industrial Informatics (2022):1-12.