Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a fuzzy ART neural network, a relatively recent neural network method, with the performance of traditional hierarchical clustering methods. For large, industry-type data sets, the fuzzy ART network, with the modifications proposed here, is capable of performance levels equal or superior to those of the widely used hierarchical clustering methods. However, like other ART networks, Fuzzy ART also results in category proliferation problems, an aspect that continues to require attention for ART networks. However, low execution times and superior solution quality make fuzzy ART a useful addition to the set of tools and techniques now available for group technology and design of cellular manufacturing systems.

[1]  Sagar V. Kamarthi,et al.  Neural networks and their applications in component design data retrieval , 1990, J. Intell. Manuf..

[2]  C. H. Dagli,et al.  Possible applications of neural networks in manufacturing , 1989, International 1989 Joint Conference on Neural Networks.

[3]  Jannes Slomp,et al.  The capacitated cell formation problem : a new hierarchical methodology , 1995 .

[4]  G. Srinivasan,et al.  GRAFICS—a nonhierarchical clustering algorithm for group technology , 1991 .

[5]  Nallan C. Suresh,et al.  An improved neural network leader algorithm for part-machine grouping in group technology , 1993 .

[6]  Jannes Slomp,et al.  Sequence-dependent clustering of parts and machines: a Fuzzy ART neural network approach , 1999 .

[7]  Behnam Malakooti,et al.  A variable-parameter unsupervised learning clustering neural network approach with application to machine-part group formation , 1995 .

[8]  Peihua Gu,et al.  A multi-constraint neural network for the pragmatic design of cellular manufacturing systems , 1995 .

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

[10]  T. T. Narendran,et al.  Machine-cell formation through neural network models , 1994 .

[11]  S. Chi,et al.  Generalized part family formation using neural network techniques , 1992 .

[12]  G. N. Lance,et al.  A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..

[13]  Cheng-Fo Sen,et al.  ART1 neural network approach to large scale group technology problems , 1992 .

[14]  S. M. Shafer,et al.  Part-Machine-Labor Grouping: The Problem and Solution Methods , 1998 .

[15]  Hamid Seifoddini,et al.  Clustering algorithms for the design of a cellular manufacturing system—an analysis for their performance , 1990 .

[16]  Andrew Kusiak,et al.  Grouping parts with a neural network , 1994 .

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

[18]  Y. Moon,et al.  An unified group technology implementation using the backpropagation learning rule of neural networks , 1991 .

[19]  T. Warren Liao,et al.  Integration of a feature-based CAD system and an ART1 neural model for GT coding and part family forming , 1994 .

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

[21]  Nallan C. Suresh,et al.  Performance of Selected Part‐Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection , 1994 .

[22]  T. T. Narendran,et al.  Neural network model for design retrieval in manufacturing systems , 1992 .

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

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

[25]  Laura I. Burke,et al.  Neural networks and the part family/ machine group formation problem in cellular manufacturing: A framework using fuzzy ART , 1995 .

[26]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[27]  Uday R. Kulkarni,et al.  Self-organizing map network as an interactive clustering tool - An application to group technology , 1995, Decis. Support Syst..

[28]  John M. Kay,et al.  Group Technology and Cellular Manufacturing , 1998 .

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

[30]  Venu Venugopal,et al.  Artificial Neural Networks and Fuzzy Models: New Tools for Part-Machine Grouping , 1998 .

[31]  Chao-Hsien Chu,et al.  Manufacturing Cell Formation by Competitive Learning , 1993 .

[32]  Utpal Roy,et al.  Connectionist models for part-family classifications , 1993 .

[33]  R. Huggahalli,et al.  Machine-part family formation with the adaptive resonance theory paradigm , 1995 .

[34]  A.M.M. Jamal,et al.  Neural Network and Cellular Manufacturing , 1993 .

[35]  N. Singh,et al.  Design of cellular manufacturing systems: An invited review , 1993 .

[36]  J. Hartigan.  Consistency of Single Linkage for High-Density Clusters , 1981 .

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

[38]  P. Gu,et al.  Expert self-organizing neural network for the design of cellular manufacturing systems , 1994 .

[39]  Asoo J. Vakharia,et al.  Designing a Cellular Manufacturing System: A Materials Flow Approach Based on Operation Sequences , 1990 .

[40]  Andrew Kusiak,et al.  Neural computing-based design of components for cellular manufacturing , 1996 .

[41]  J. Witte.  The use of similarity coefficients in production flow analysis , 1980 .

[42]  Young B. Moon,et al.  Forming part-machine families for cellular manufacturing: A neural-network approach , 1990 .

[43]  J. M. Kay,et al.  Group technology and cellular manufacturing: state-of-the-art synthesis of research and practice , 1998 .

[44]  Paul J. Schweitzer,et al.  Problem Decomposition and Data Reorganization by a Clustering Technique , 1972, Oper. Res..

[45]  R. Selvam,et al.  Algorithmic grouping of operation sequences , 1985 .

[46]  G. W. Milligan,et al.  A Study of the Beta-Flexible Clustering Method. , 1989, Multivariate behavioral research.

[47]  T. Warren Liao,et al.  An evaluation of ART1 neural models for GT part family and machine cell forming , 1993 .

[48]  Satheesh Ramachandran,et al.  A self-organizing neural network approach for the design of cellular manufacturing systems , 1992, J. Intell. Manuf..

[49]  Sung-Lyong Kang,et al.  A work load-oriented heuristic methodology for manufacturing cell formation allowing reallocation of operations , 1993 .

[50]  S. Kamal,et al.  FACT : A new neural network-based clustering algorithm for group technology , 1996 .

[51]  G. Barreto,et al.  A SELF-ORGANIZING NEURAL NETWORK , 2000 .

[52]  Nallan C. Suresh,et al.  Performance of Fuzzy ART neural network for group technology cell formation , 1994 .

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

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

[55]  Kenneth R. Currie.  An intelligent grouping algorithm for cellular manufacturing , 1992 .

[56]  David F. Rogers,et al.  Similarity and distance measures for cellular manufacturing. Part II. An extension and comparison , 1993 .

[57]  N. Suresh,et al.  Machine-component cell formation in group technology : a neural network approach , 1992 .

[58]  T. T. Narendran,et al.  CASE: A clustering algorithm for cell formation with sequence data , 1998 .

[59]  S. H. Huang,et al.  Applications of neural networks in manufacturing: a state-of-the-art survey , 1995 .

[60]  Chuen-Sheng Cheng,et al.  A neural network-based cell formation algorithm in cellular manufacturing , 1995 .

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

[62]  M. Chandrasekharan,et al.  ZODIAC—an algorithm for concurrent formation of part-families and machine-cells , 1987 .

[63]  Lakhmi C. Jain,et al.  Self-Organizing Neural Networks , 2002 .

[64]  K. Y. Tam,et al.  An operation sequence based similarity coefficient for part families formations , 1990 .

[65]  Uday R. Kulkarni,et al.  Dynamic grouping of parts in flexible manufacturing systems : a self-organizing neural networks approach , 1995 .

[66]  Utpal Roy,et al.  Learning group-technology part families from solid models by parallel distributed processing , 1992 .

[67]  N.-E. Dahel.  Design of cellular manufacturing systems in tandem configuration , 1995 .

[68]  Chao-Hsien Chu.  Cluster analysis in manufacturing cellular formation , 1989 .

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