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	An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes
	Liu B. ; Wang L. ; Liu Y. ; Qian B. ; Jin Y. H.
	2010
关键词	Particle swarm optimization Polypropylene batch Batch scheduling Multi-stage flow shop Hybrid flow shop Simulated annealing Zero-wait No intermediate storage of-the-art flowshop sequencing problem continuous-time memetic algorithms chemical-processes genetic algorithm plants systems models formulations
英文摘要	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
出处	Computers & Chemical Engineering
卷	34
期	4
页	518-528
收录类别	SCI
语种	英语
ISSN号	0098-1354
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/23520]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Liu B.,Wang L.,Liu Y.,et al. An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes. 2010.

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