An improved fuzzy c-means algorithm for manufacturing cell formation

This paper presents an improved fuzzy c-means algorithm to solve the manufacturing cell formation problems. The proposed algorithm, which integrates the subtractive algorithm (to produce an initial solution), the fuzzy c-means (FCM) algorithm and a solution selecting procedure (to identify the best solution), remedies the major weaknesses of original FCM clustering. We test the performance of the proposed algorithm with 20 data sets from open literature and 60 generated data sets. Our experiments show that the proposed approach performs much better than the original FCM and the solutions are consistent with the best solutions found in references or the control solutions.

[1]  Kai-Ling Mak,et al.  An Adaptive Genetic Algorithm for Manufacturing Cell Formation , 2000 .

[2]  T. Liao,et al.  Integrated use of fuzzy c-means and fuzzy KNN for GT part family and machine cell formation , 2000 .

[3]  Jacob Jen-Gwo Chen,et al.  Fuzzy-set-based machine-cell formation in cellular manufacturing , 1996, J. Intell. Manuf..

[4]  Asoo J. Vakharia,et al.  Cell formation in group technology: review, evaluation and directions for future research , 1998 .

[5]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[6]  R. Rajagopalan,et al.  A multidimensional scaling algorithm for group layout in cellular manufacturing , 1993 .

[7]  Chao-Hsien Chu,et al.  An improved neural network for manufacturing cell formation , 1997, Decis. Support Syst..

[8]  S. Susanto,et al.  A new fuzzy-c-means and assignment technique-based cell formation algorithm to perform part- type clusters and machine-type clusters separately , 1999 .

[9]  Rajesh N. Dave,et al.  Application of noise clustering in group technology , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[10]  Sunderesh S. Heragu,et al.  Group Technology and Cellular Manufacturing , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[11]  Nallan C. Suresh,et al.  A neural network system for shape-based classification and coding of rotational parts , 1991 .

[12]  Young B. Moon,et al.  Forming part-machine families for cellular manufacturing: A neural-network approach , 1990 .

[13]  Tarun Gupta,et al.  Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system , 1990 .

[14]  M. Chandrasekharan,et al.  Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology , 1990 .

[15]  Chao-Hsien Chu,et al.  Manufacturing Cell Formation by Competitive Learning , 1993 .

[16]  Luca Settineri,et al.  An Application of Fuzzy Clustering to Cellular Manufacturing , 1997 .

[17]  Andrew Kusiak,et al.  Grouping of parts and components in flexible manufacturing systems , 1986 .

[18]  Keith Case,et al.  Component grouping for GT applications—a fuzzy clustering approach with validity measure , 1995 .

[19]  M. Chandrasekharan,et al.  An ideal seed non-hierarchical clustering algorithm for cellular manufacturing , 1986 .

[20]  Haiping Xu,et al.  Part family formation for GT applications based on fuzzy mathematics , 1989 .

[21]  Young B. Moon,et al.  Analysis of part families for group technology applications using decision trees , 1997 .

[22]  Chuen-Sheng Cheng,et al.  A neural network-based cell formation algorithm in cellular manufacturing , 1995 .

[23]  Chun Zhang,et al.  Concurrent formation of part families and machine cells based on the fuzzy set theory , 1992 .

[24]  Chao-Hsien Chu,et al.  A fuzzy clustering approach to manufacturing cell formation , 1991 .