A hybrid neural network approach to cell formation in cellular manufacturing

Abstract

Abstract: The design of Cellular Manufacturing Systems (CMS) has attained the significant interest of academicians, researchers and practitioners over the last three decades. CMS is regarded as an efficient production strategy for batch type of production. Literature suggests that since the last two decades neural network based methods have been intensively used in cell formation problems while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART-Centroid Linkage Clustering Technique (FACLCT), to solve the part-machine grouping problems in cellular manufacturing systems considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared with the existing clustering models such as simple C-Linkage, K-Means, modified ART1 and genetic algorithm and achieved better performance. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.