Machine-component cell formation in group technology : a neural network approach

SUMMARY This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network is selected due to the fact that this clustering method utilizes binary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offers the capability of handling large, industry-size data sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained were found to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experiments on larger data sets, involving 10000 parts by 100 machine types, revealed that the method results in the identification of clusters with fast execution times. If a block diagonal structure existed in the input data, it was identified to a good degree of perfection. It was also found to be efficient with some imperfections i...

[1]  John L. Burbidge,et al.  Production flow analysis , 1963 .

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

[3]  I.G.K. Ei-Essawy,et al.  Component flow analysis - an effective approach to production systems' design , 1972 .

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

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

[6]  R. Rajagopalan,et al.  Design of cellular production systems A graph-theoretic approach , 1975 .

[7]  J. de Witte,et al.  Production flow synthesis , 1978 .

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

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

[10]  P. Waghodekar,et al.  Machine-component cell formation in group technology: MACE , 1984 .

[11]  Larry R. Taube,et al.  Weighted similarity measure heuristics for the group technology machine clustering problem , 1985 .

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

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

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

[15]  S. Grossberg,et al.  Neural Dynamics of Category Learning and Recognition: Attention, Memory Consolidation, and Amnesia , 1987 .

[16]  Vinod Kumar,et al.  Entropic measures of manufacturing flexibility , 1987 .

[17]  A. Kusiak,et al.  Efficient solving of the group technology problem , 1987 .

[18]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[19]  R. Lippmann.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[20]  C. Mosier.  An experiment investigating the application of clustering procedures and similarity coefficients to the GT machine cell formation problem , 1989 .

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

[22]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

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

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

[25]  Nallan C. Suresh.  Partitioning Work Centers for Group Technology: Insights from an Analytical Model* , 1991 .

[26]  Nallan C. Suresh.  Partitioning Work Centers for Group Technology: Analytical Extension and Shop-Level Simulation Investigation* , 1992 .