Part family identification using a simple genetic algorithm

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed.

[1]  Andrew Kusiak,et al.  Group technology. Analysis of selected models and algorithms , 1991 .

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

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

[4]  O. Felix Offodile.  Application of similarity coefficient method to parts coding and classification analysis in group technology , 1991 .

[5]  Young B. Moon,et al.  Establishment of a neurocomputing model for part family/machine group identification , 1992, J. Intell. Manuf..

[6]  Andrew Mcgilvary Gillies,et al.  Machine Learning Procedures for Generating Image Domain Feature Detectors , 1985 .

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

[8]  Nancy Lea Hyer,et al.  Group technology in the US manufacturing industry: A survey of current practices , 1989 .

[9]  John E. Biegel,et al.  Genetic algorithms and job shop scheduling , 1990 .

[10]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[11]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations , 1987, ICGA.

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

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

[14]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[15]  Mohan V. Tatikonda,et al.  Adoption and implementation of group technology classification and coding systems : insights from seven case studies , 1992 .

[16]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[17]  Jong-Yun Jung,et al.  FORCOD: A coding and classification system for formed parts , 1991 .

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

[19]  Joseph El Gomayel,et al.  GROUP TECHNOLOGY AND PRODUCTIVITY , 1986 .

[20]  Daniel T. Koenig.  Manufacturing Engineering: Principles for Optimization , 1997 .

[21]  T. J. Greene,et al.  A review of cellular manufacturing assumptions, advantages and design techniques , 1984 .