A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows

doi:10.1016/j.eswa.2022.116921

	A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows
	Zhu, Ningning 2; Zhao, Fuqing 2; Wang, Ling 3; Ding, Ruiqing 2; Xu, Tianpeng 2; Jonrinaldi 1
刊名	Expert Systems with Applications
	2022-07-15
卷号	198
关键词	Fruits Iterative methods Job shop scheduling Learning algorithms Local search (optimization) Machine shop practice Manufacture Distributed no-wait flow-shop Due-window Fruit fly optimization Fruitflies Knowledge model No-wait flowshop Optimisations Probability knowledge model Variable neighborhood descend Variable neighbourhoods
ISSN号	0957-4174
DOI	10.1016/j.eswa.2022.116921
英文摘要	The distributed no-wait flow shop scheduling problem with due windows (DNWFSPDW) is a novel and considerable model for modern production chain and large manufacturing industry. The object of total weighted earliness and tardiness (TWETdw) is a common cost indicator in application. A discrete knowledge-guided learning fruit fly optimization algorithm (DKLFOA) is proposed in this study to minimize TWET in DNWFSPDW. A knowledge-based structural initialization method (KNEHdw) is proposed to construct an effective initial solution. In the KNEHdw, the property that the job has no waiting time between processing machines in the no-wait flow shop scheduling problem is abstracted as knowledge to instruct jobs to be placed in possible positions. The swarm center expands from a single individual to an elitist swarm in the vision search stage. A probability knowledge model is established based on the sequence relationship of jobs in the elite population. The feedback information in the iterative process using the probabilistic knowledge model leads the population to search in the direction with a high success rate. The inferior individuals are allocated to the corresponding elite individuals for the local search in the olfactory search stage. The knowledge of weight in due windows is utilized to avoid invalid search during the iteration process. The variable neighborhood descent (VND) strategy is adopted in the local search to enhance the accuracy of the proposed algorithm and jump out of the local optimal. The design of experimental method (DOE) is introduced to calibrate the parameters in the algorithm. The simulation results show that DKLFOA has advantages for solving DNWFSPDW problems comparing with the state-of-the-art algorithms. © 2022 Elsevier Ltd
语种	英语
出版者	Elsevier Ltd
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/157896]
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.Department of Industrial Engineering, Universitas Andalas, Padang; 25163, Indonesia 2.School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou; 730050, China; 3.Department of Automation, Tsinghua University, Beijing; 10084, China;
推荐引用方式 GB/T 7714	Zhu, Ningning,Zhao, Fuqing,Wang, Ling,et al. A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows[J]. Expert Systems with Applications,2022,198.
APA	Zhu, Ningning,Zhao, Fuqing,Wang, Ling,Ding, Ruiqing,Xu, Tianpeng,&Jonrinaldi.(2022).A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows.Expert Systems with Applications,198.
MLA	Zhu, Ningning,et al."A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows".Expert Systems with Applications 198(2022).