Abstract
AbstractThe aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.KeywordsGroup technologyPart-machine clusteringGrouping efficiencyAdaptive resonance theory neural networkAnt colony system
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