A self-organizing neural network approach for the design of cellular manufacturing systems

The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.

[1]  M. Wann,et al.  The influence of training sets on generalization in feed-forward neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[2]  W. Rudin.  Real and complex analysis, 3rd ed. , 1987 .

[3]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[4]  Moshe M. Barash,et al.  Design of a cellular manufacturing system: A syntactic pattern recognition approach , 1986 .

[5]  J. King.  Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm , 1980 .

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

[7]  D. A. Milner,et al.  Direct clustering algorithm for group formation in cellular manufacture , 1982 .

[8]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[9]  Harold J. Steudel,et al.  A dynamic programming based heuristic for machine grouping in manufacturing cell formation , 1987 .

[10]  P. Bhagat,et al.  An introduction to neural nets , 1990 .

[11]  Inyong Ham,et al.  Multiobjective cluster analysis for part family formations , 1986 .

[12]  Hoda A. ElMaraghy,et al.  Feature based expert parts assignment in cellular manufacturing , 1989 .

[13]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[14]  Andrew Kusiak,et al.  Artificial Intelligence And Operations Research In Flexible Manufacturing Systems , 1987 .

[15]  Satheesh Ramachandran,et al.  Neural network-based design of cellular manufacturing systems , 1991, J. Intell. Manuf..

[16]  W. Rudin.  Real and complex analysis , 1968 .

[17]  T. Narendran,et al.  An assignment model for the part-families problem in group technology , 1990 .

[18]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[19]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[20]  John L. Burbidge,et al.  The introduction of group technology , 1975 .

[21]  J. King,et al.  Machine-component group formation in group technology: review and extension , 1982 .

[22]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[23]  A. Kusiak.  The generalized group technology concept , 1987 .

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

[25]  Bart Kosko,et al.  Unsupervised learning in noise , 1990, International 1989 Joint Conference on Neural Networks.

[26]  R. S. Lashkari,et al.  Machine grouping problem in cellular manufacturing systems ― an integer programming approach , 1989 .

[27]  F. Fred Choobineh,et al.  A framework for the design of cellular manufacturing systems , 1988 .