期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 61, 期 3, 页码 529-541出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2011.04.008
关键词
Pareto genetic algorithm; NSGA-II; Reentrant hybrid flowshop
资金
- Samsung SDS, Seoul, Korea
- National Research Foundation of Korea [2007-331-D00539, 2011-0016598, 2009-0069349] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper deals with a scheduling problem for reentrant hybrid flowshop with serial stages where each stage consists of identical parallel machines. In a reentrant flowshop, a job may revisit any stage several times. Local-search based Pareto genetic algorithms with Minkowski distance-based crossover operator is proposed to approximate the Pareto optimal solutions for the minimization of makespan and total tardiness in a reentrant hybrid flowshop. The Pareto genetic algorithms are compared with existing multi-objective genetic algorithm, NSGA-II in terms of the convergence to optimal solution, the diversity of solution and the dominance of solution. Experimental results show that the proposed crossover operator and local search are effective and the proposed algorithm outperforms NSGA-II by statistical analysis. (C) 2011 Elsevier Ltd. All rights reserved.
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