Spectral Clustering for Cell Formation with Minimum Dissimilarities Distance

Group Technology (GT) is a useful tool in manufacturing systems. Cell formation (CF) is a part of a cellular manufacturing system that is the implementation of GT. It is used in designing cellular manufacturing systems using the similarities between parts in relation to machines so that it can identify part families and machine groups. Spectral clustering had been applied in CF, but, there are still several drawbacks to these spectral clustering approaches. One of them is how to get an optimal number of clusters/cells. To address this concern, we propose a spectral clustering algorithm for machine-part CF using minimum dissimilarities distance. Some experimental examples are used to illustrate its efficiency. In summary, the proposed algorithm has better efficiency to be used in CF with a wide variety of machine/part matrices.

[1]  Miin-Shen Yang,et al.  Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications , 2006 .

[2]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[4]  Miin-Shen Yang,et al.  Machine-part cell formation in group technology using a modified ART1 method , 2008, Eur. J. Oper. Res..

[5]  Kuo-Lung Wu,et al.  Mean shift-based clustering , 2007, Pattern Recognit..

[6]  Pedro M. Vilarinho,et al.  A simulated annealing approach for manufacturing cell formation with multiple identical machines , 2003, Eur. J. Oper. Res..

[7]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[8]  S. Oliveira,et al.  A spectral clustering algorithm for manufacturing cell formation , 2009, Comput. Ind. Eng..

[9]  Miin-Shen Yang A survey of fuzzy clustering , 1993 .

[10]  David Pollard,et al.  Quantization and the method of k -means , 1982, IEEE Trans. Inf. Theory.

[11]  Rakesh Nagi,et al.  An efficient heuristic in manufacturing cell formation for group technology applications , 1990 .

[12]  Panos M. Pardalos,et al.  Cell Formation in Industrial Engineering: Theory, Algorithms and Experiments , 2013 .

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

[14]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[15]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[16]  S. L. Hakimi,et al.  Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph , 1964 .

[17]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[18]  Serafettin Alpay,et al.  A metaheuristic approach for a cubic cell formation problem , 2016, Expert Syst. Appl..

[19]  Divakar Rajamani,et al.  Cellular Manufacturing Systems , 1996 .

[20]  Boris Goldengorin,et al.  A Computational Study of the Pseudo-Boolean Approach to the p-Median Problem Applied to Cell Formation , 2011, INOC.

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

[22]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Spectral methods for graph clustering - A survey , 2011, Eur. J. Oper. Res..