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

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.