A particle swarm optimization algorithm for part-machine grouping

被引:43
作者
Andres, Carlos
Lozano, Sebastian
机构
[1] Univ Politecn Valencia, Ind Management Dept, CIGIP Res Ctr, Valencia 46022, Spain
[2] Univ Seville, Ind Management Dept, Seville 41092, Spain
关键词
cellular manufacturing; part-machine grouping problem; particle swarm optimization;
D O I
10.1016/j.rcim.2005.11.013
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Although in the last years different metaheuristic methods have been used to solve the cell formation problem in group technology, this paper presents the first particle swarm optimization (PSO) algorithm designed to address this problem. PSO is a population-based evolutionary computation technique based on a social behavior metaphor. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. A maximum cell size is imposed. Some published exact results have been used as benchmarks to assess the proposed algorithm. The computational results show that the PSO, algorithm is able to find the optimal solutions on almost all instances. (C) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:468 / 474
页数:7
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