Minimizing total intercell and intracell moves in cellular manufacturing: a genetic algorithm approach

Abstract Cellular manufacturing lias been hailed as an effective way lo improve productivity in parts manufacturing organizations. The objective of cellular manufacturing is to group ptirts that have similar processing requirements into part families and machines into groups (cells) which meet the processing needs of part families assigned to them. A problem is formulated which minimizes the intercell and intracell part movements, and attention is focused on the minimum acceptable level of machine utilization while selecting an assignment of parts to a cell. Machine-cell-part-grouping problems are solved for two, three and four cells with an approach based on genetic algorithms. The results arc superior to the previous best in the published literature.

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

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

[3]  Jerry C. Wei,et al.  An Optimal Model for Cell Formation Decisions , 1990 .

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

[5]  Hui-Chuan Chen,et al.  A network approach to cell formation in cellular manufacturing , 1990 .

[6]  Larry E. Stanfel,et al.  Machine clustering for economic production , 1985 .

[7]  Henry C. Co,et al.  Configuring cellular manufacturing systems , 1988 .

[8]  Ronald G. Askin,et al.  A Hamiltonian path approach to reordering the part-machine matrix for cellular manufacturing , 1991 .

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

[10]  Ronald G. Askin,et al.  A graph partitioning procedure for machine assignment and cell formation in group technology , 1990 .

[11]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[14]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[15]  David F. Rogers,et al.  A goal programming approach to the cell formation problem , 1991 .

[16]  Andrew Kusiak,et al.  Group technology , 1987 .

[17]  Harold J. Steudel,et al.  A within-cell utilization based heuristic for designing cellular manufacturing systems , 1987 .

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

[19]  Gunar E. Liepins,et al.  Genetic algorithms: Foundations and applications , 1990 .

[20]  F. Boctor A Jinear formulation of the machine-part cell formation problem , 1991 .

[21]  Sheh-Pang Wu,et al.  The synthetic index algorithm: an improved cluster analysis procedure for machine cell formation , 1990 .

[22]  Rasaratnam Logendran,et al.  A workload based model for minimizing total intercell and intracell moves in cellular manufacturing , 1990 .

[23]  M. Chandrasekharan,et al.  MODROC: an extension of rank order clustering for group technology , 1986 .

[24]  Nancy Lea Hyer,et al.  Cellular manufacturing in the U.S. industry: a survey of users , 1989 .

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

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

[27]  Larry R. Taube,et al.  The facets of group technology and their impacts on implementation--A state-of-the-art survey , 1985 .