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An improved ant-colony algorithm for the grouping of machine-cells and part-families in cellular manufacturing systems

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  • N. Megala
  • Chandrasekharan Rajendran

Abstract

In the present study, we address the machine-cell design and part-family formation problem in cellular manufacturing systems. The objective of the study is to group machines into machine-cells and parts into part-families such that the grouping efficacy is maximised. We propose an improved ant-colony optimisation (IACO) algorithm to obtain machine-cells and part-families. The performance of the algorithm is tested by using benchmark datasets available in the literature. The grouping efficacy obtained by the proposed algorithm is compared with the grouping efficacies obtained by the existing approaches present in the literature. The comparative analysis shows that the proposed IACO performs very well in maximising the grouping efficacy.

Suggested Citation

  • N. Megala & Chandrasekharan Rajendran, 2013. "An improved ant-colony algorithm for the grouping of machine-cells and part-families in cellular manufacturing systems," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 17(3), pages 345-373.
    • Handle: RePEc:ids:ijores:v:17:y:2013:i:3:p:345-373
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    Cited by:

    1. Imen Zaabar & Vladimir Polotski & Léon Bérard & Boujemaa El-Ouaqaf & Yvan Beauregard & Marc Paquet, 2022. "A two-phase part family formation model to optimize resource planning: a case study in the electronics industry," Operational Research, Springer, vol. 22(4), pages 4441-4469, September.

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