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Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/73498


Title: A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem
Authors: 陳春龍
Chen, Chun-Lung;Huang, Shin-Ying;Tzeng, Yeu-Ruey;Chen, Chuen-Lung
Contributors: 資管系
Keywords: Permutation flow-shop scheduling problem;Particle swarm optimization;Makespan
Date: 2013-12
Issue Date: 2015-02-12 14:19:38 (UTC+8)
Abstract: This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms.
Relation: Soft Computing,18(11),2271-2282
Data Type: article
DOI link: http://dx.doi.org/10.1007/s00500-013-1199-z
Appears in Collections:[Department of MIS] Periodical Articles

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