This research presents the usage of a genetic algorithm for the clustering of parts and machines. A detailed analysis is shown comparing GCA results with single link cluster analysis, rank order clustering, and the direct clustering algorithm. GCA was also compared with several additional cell formation heuristics described in the recent literature, including GRAPHICS, MODROC, and a cost-based heuristic. Results showed that the GCA was far superior over single link cluster analysis and provided equivalent results to those of the direct clustering algorithm and rank order clustering. GCA was also found to provide superior results to the other heuristics. The discussion explains these findings by illustrating the inflexibility of traditional cell formation heuristics in the selection of final machine-component groupings.
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Billo, R.E., Bidanda, B. & Tate, D. A genetic cluster algorithm for the machine-component grouping problem. J Intell Manuf 7, 229–241 (1996). https://doi.org/10.1007/BF00118082
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DOI: https://doi.org/10.1007/BF00118082