An In-depth Analysis of Fuzzy C-Means Clustering for Cellular Manufacturing

Fuzzy c-means (FCM), a well-known clustering algorithm, has been successfully adapted to solve a variety of applications including cellular manufacturing. This paper provides an in-depth analysis on the deficiencies of applying FCM to solve the cell formation (CF) problem in cellular manufacturing and proposes ways of enhancing its performance. A large-scale experiment is conducted to evaluate the effects of different enhancements over FCM. Our study shows that, for CF problem, (1) the proposed distance function has the largest impact on solution quality, followed by the subtractive initialization, (2) the effects of center function and solution selection are not as significant as the formers, and (3) combining the proposed distance function and subtractive initialization with FCM produces the most synergic effects in improving solution quality, while only adding a tolerable amount of computation time.