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

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 a...

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Veröffentlicht in:Computers & operations research 2010-12, Vol.37 (12), p.2071-2081
Hauptverfasser: Li, Xiangyong, Baki, M.F., Aneja, Y.P.
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Sprache:eng
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Zusammenfassung: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.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2010.02.007