Comparative study of similarity coefficients and clustering algorithms in cellular manufacturing

Abstract Three components of a machine cell formation process—similarity coefficients, clustering algorithms, and performance measures—are studied. A new performance measure is introduced and a comparative study of three different similarity coefficients—the Jaccard's similarity coefficient, weighted similarity coefficient, and commonality score—is conducted.

[1]  Anthony Vannelli,et al.  Strategic subcontracting for efficient disaggregated manufacturing , 1986 .

[2]  Philip M. Wolfe,et al.  Application of the Similarity Coefficient Method in Group Technology , 1986 .

[3]  Philip M. Wolfe,et al.  Selection of a Threshold Value Based on Material Handling Cost in Machine-Component Grouping , 1987 .

[4]  M. Chandrasekharan,et al.  GROUPABIL1TY: an analysis of the properties of binary data matrices for group technology , 1989 .

[5]  R. Sokal,et al.  Principles of numerical taxonomy , 1965 .

[6]  Jerry C. Wei,et al.  Commonality analysis: A linear cell clustering algorithm for group technology , 1989 .

[7]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[8]  Andrew Kusiak,et al.  An Efficient Cluster Identification Algorithm , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  John McAuley,et al.  Machine grouping for efficient production , 1972 .

[10]  M. Aldenderfer.  Cluster Analysis , 1984 .

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

[12]  Nancy Lea Hyer,et al.  Research issues in cellular manufacturing , 1987 .

[13]  Allan S. Carrie,et al.  Numerical taxonomy applied to group technology and plant layout , 1973 .

[14]  Hamid Seifoddini,et al.  A note on the similarity coefficient method and the problem of improper machine assignment in group technology applications , 1989 .