Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect

	Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect
	Pengyu Xie; Xin Chen; Vincent Chau; Malgorzata Sterna; Jacek Blazewicz
刊名	Computers & Industrial Engineering
	2017
文献子类	期刊论文
英文摘要	Late work minimization is one of the newer branches in the scheduling theory, with the goal of minimizing the total size of late parts of all jobs in the system. In this paper, we study the scheduling problem in flow shop, which finds many practical applications. First, we prove that the problem with three machines and a common due date is NP-hard in the strong sense. Then we extend this basic model, considering the problem with the arbitrary number of machines, various due dates and learning effect, and propose a particle swarm optimization algorithm (PSO). Computational experiments show that the PSO is an efficient method for solving the problem under consideration, both from algorithm-performance and time-consumption views.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12471]
专题	深圳先进技术研究院_数字所
作者单位	Computers & Industrial Engineering
推荐引用方式 GB/T 7714	Pengyu Xie,Xin Chen,Vincent Chau,et al. Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect[J]. Computers & Industrial Engineering,2017.
APA	Pengyu Xie,Xin Chen,Vincent Chau,Malgorzata Sterna,&Jacek Blazewicz.(2017).Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect.Computers & Industrial Engineering.
MLA	Pengyu Xie,et al."Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect".Computers & Industrial Engineering (2017).