Ant colony optimization with an application in Cellular Manufacturing

Abstract

Cellular Manufacturing is one of the major applications of group technology. It requires an effective part clustering approach to execute preliminary manufacturing cell design. One of famous approaches is the cluster analysis method, which uses similarity coefficients and clustering methods to group similarity parts into part families. Clustering methods are divided into two categories: hierarchical and nonhierarchical methods. Hierarchical methods often suffer from chaining effects, while nonhierarchical methods need a predetermined cluster number. The research proposes a part clustering algorithm that is based on an artificial ant clustering model. The algorithm utilizes the characteristics of ants, congregation and randomness, to prevent grouping results from being fixed during clustering processes and to reduce the effects of noisy data.