An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes

doi:10.1016/j.compchemeng.2009.12.010

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	An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes
	Liu, Bo 1,2,3; Wang, Ling 1; Liu, Ying 4; Qian, Bin 5; Jin, Yi-Hui 1
	2010-04-05
发表期刊	COMPUTERS & CHEMICAL ENGINEERING
ISSN	0098-1354
卷号	34 期号:4 页码:518-528
摘要	Short-term scheduling for batch processes which allocates a set of limited resources over time to manufacture one or more products plays a key role in batch processing systems of the enterprise for maintaining competitive position in fast changing market. This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm for polypropylene (PP) batch industries to minimize the maximum completion time, which is modeled as a complex generalized multi-stage flow shop scheduling problem with parallel units at each stage and different inventory storage policies In HPSO. a novel encoding scheme based on random key representation, a new assignment scheme STPT (smallest starting processing time) by taking the different intermediate storage strategies into account, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing with an adaptive meta-Lamarckian learning strategy are proposed Simulation results based on a set of random instances and comparisons with several adaptations of constructive methods and meta-heuristics demonstrate the effectiveness of the proposed HPSO (C) 2010 Elsevier Ltd. All rights reserved
关键词	Particle swarm optimization Polypropylene batch Batch scheduling Multi-stage flow shop Hybrid flow shop Simulated annealing Zero-wait No intermediate storage
DOI	10.1016/j.compchemeng.2009.12.010
语种	英语
资助项目	National Science Foundation of China[70871065] ; National Science Foundation of China[60834004] ; National Science Foundation of China[60774082] ; National Science Foundation of China[60704032] ; National Science Foundation of China[60904081]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Chemical
WOS记录号	WOS:000276426900011
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/10614
专题	中国科学院数学与系统科学研究院
通讯作者	Liu, Bo
作者单位	1.Tsinghua Univ, Dept Automat, TNList, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Ctr Forecasting Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Ctr Chinese Agr Policy, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Beijing Jiaotong Univ, CCISR, Beijing 100044, Peoples R China 5.Kunming Univ Sci & Technol, Dept Automat, Kunming 650051, Peoples R China
推荐引用方式 GB/T 7714	Liu, Bo,Wang, Ling,Liu, Ying,et al. An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes[J]. COMPUTERS & CHEMICAL ENGINEERING,2010,34(4):518-528.
APA	Liu, Bo,Wang, Ling,Liu, Ying,Qian, Bin,&Jin, Yi-Hui.(2010).An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes.COMPUTERS & CHEMICAL ENGINEERING,34(4),518-528.
MLA	Liu, Bo,et al."An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes".COMPUTERS & CHEMICAL ENGINEERING 34.4(2010):518-528.