An ant colony optimization metaheuristic for machine-part cell formation problems

Authors:
Xiangyong Li;M. F. Baki;Y. P. Aneja
Affiliations:
School of Economics & Management, Tongji University, Shanghai 200092, China;Odette School of Business, University of Windsor, Windsor, Ontario, Canada N9B 3P4;Odette School of Business, University of Windsor, Windsor, Ontario, Canada N9B 3P4
Venue:
Computers and Operations Research
Year:
2010

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Abstract

In this paper we propose an ant colony optimization metaheuristic (ACO-CF) to solve the machine-part cell formation problem. ACO-CF is a MAX-MIN ant system, which is implemented in the hyper-cube framework to automatically scale the objective functions of machine-part cell formation problems. As an intensification strategy, we integrate an iteratively local search into ACO-CF. Based on the assignment of the machines or parts, the local search can optimally reassign parts or machines to cells. We carry out a series of experiments to investigate the performance of ACO-CF on some standard benchmark problems. The comparison study between ACO-CF and other methods proposed in the literature indicates that ACO-CF is one of the best approaches for solving the machine-part cell formation problem.