Cell formation with workload data in cellular manufacturing system using genetic algorithm

Abstract

Cellular Manufacturing System (CMS) is regarded as an efficient production strategy for batch type of production. CMS rests on the principle of grouping the machines into machine cells and parts into part families based on suitable similarity criteria. Usually zero-one machine-part incidence matrix (MPIM) obtained from the route sheet information is used to form machine cells. In this paper, an attempt has been made to solve the cell formation problem considering work load data and a genetic algorithm (GA) is suggested to form machine cells and part families. The performance of the proposed algorithm is compared with existing algorithms such as K-means algorithm and modified ART1 algorithm found in the literature using a newly defined performance measure known as modified grouping efficiency (MGE). The proposed algorithm is tested with problems from open literature and the results are compared with the existing algorithms found in the literature. The results support the better performance of the proposed algorithm.