COMPONENT GROUPING FOR GT APPLICATIONS - A FUZZY CLUSTERING APPROACH WITH VALIDITY MEASURE

被引:40
作者
GINDY, NNZ
RATCHEV, TM
CASE, K
机构
[1] Department of Manufacturing Engineering and Operations Management, University of Nottingham, University Park, Nottingham
[2] Department of Manufacturing Engineering, Loughborough University of Technology, Loughborough, Leicester
关键词
D O I
10.1080/00207549508904828
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The variety of the currently available component grouping methodologies and algorithms provide a good theoretical basis for implementing GT principles in cellular manufacturing environments. However, the practical application of the grouping approaches can be further enhanced through extensions to the widely used grouping algorithms and the development of criteria for partitioning components into an 'optimum' number of groups. Extensions to the fuzzy clustering algorithm and a definition of a new validity measure are proposed in this paper. These are aimed at improving the practical applicability of the fuzzy clustering approach for family formation in cellular manufacturing environments. Component partitioning is based upon assessing the compactness of components within a group and overlapping between the component groups. The developed grouping methodology is experimentally demonstrated using an industrial case study and several well known component grouping examples from the published literature.
引用
收藏
页码:2493 / 2509
页数:17
相关论文
共 23 条
[1]  
Bezdek I., Numerical taxonomy with fuzzy sets, Journal of Mathematical Biology, 1, pp. 57-71, (1975)
[2]  
Bezdek I., A convergence theorem for the fuzzy ISODATA clustering algorithms, IEEE Transactions of Pattern Analysis and Machine Intelligence, 2, 1, pp. 1-8, (1980)
[3]  
Bezdek J., Analysis of Fuzzy Information, 3, Applications in Engineering and Science, (1987)
[4]  
Cannon R., Jttendra V.D., Bezdek J., Efficient implementation of the fuzzy C-means clustering algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 2, pp. 248-255, (1986)
[5]  
Chu C.H., Hayya J.C., A fuzzy clustering approach to manufacturing cell formation, International Journal of Production Research, 29, 7, pp. 1475-1487, (1991)
[6]  
Davies D.L., Bouldin D., A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1, 2, pp. 224-227, (1979)
[7]  
Dunn J.C., Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics, 4, pp. 95-104, (1974)
[8]  
Gongaware T.A., Ham I., Cluster analysis applications for group technology manufacturing systems. 9th North American Manufacturing Research Conference (NAMARC) Proceedings, Society of Manufacturing Engineers, pp. 503-508, (1991)
[9]  
Gath I., Geva A.B., Unsupervised optimal fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, pp. 773-781, (1989)
[10]  
Jain A.K., Handbook of Pattern Recognition and Image Processing, (1988)