A genetic algorithm for integrated cell formation and layout decisions

Presents a hierarchical genetic algorithm (GA) to solve the cell formation and layout decisions of cellular manufacturing. The intrinsic features of our proposed GA include using a hierarchical chromosome structure to encode concurrent cell design and layout decisions, developing a new selection scheme to dynamically consider two highly correlated fitness functions, and proposing a group mutation operator to increase the probability of mutation. Our tests show that these modifications are fairly effective in improving solution quality as well as shortening the speed of convergence.

[1]  R. Rajagopalan,et al.  A multidimensional scaling algorithm for group layout in cellular manufacturing , 1993 .

[2]  Attahiru Sule Alfa,et al.  Integrating the grouping and layout problems in cellular manufacturing systems , 1992 .

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

[4]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[5]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[6]  Chih-Ming Hsu,et al.  Multi-objective machine-component grouping in cellular manufacturing: a genetic algorithm , 1998 .

[7]  Kar Yan Tam,et al.  Solving facility layout problems with geometric constraints using parallel genetic algorithms: Experimentation and findings , 1998 .

[8]  R. Poli.  Why the schema theorem is correct also in the presence of stochastic effects , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[9]  H. Kaebernick,et al.  Cell formation and layout designs in a cellular manufacturing environment a case study , 2000 .

[10]  Chao-Hsien Chu,et al.  Recent advances in mathematical programming for cell formation , 1995 .

[11]  Lee Altenberg,et al.  The Schema Theorem and Price's Theorem , 1994, FOGA.

[12]  K. Al-Sultan,et al.  A genetic algorithm for thepart family formation problem , 1997 .

[13]  Nancy Lea Hyer,et al.  Procedures for the part family/machine group identification problem in cellular manufacturing , 1986 .

[14]  A. A. Islier.  A genetic algorithm approach for multiple criteria facility layout design , 1998 .

[15]  L. A. Al-Hakim,et al.  On solving facility layout problems using genetic algorithms , 2000 .

[16]  Sunderesh S. Heragu,et al.  Grouping and placement of machine cells , 1997 .

[17]  Sam Kwong,et al.  Genetic Algorithms : Concepts and Designs , 1998 .

[18]  T. Narendran,et al.  A genetic algorithm approach to the machine-component grouping problem with multiple objectives , 1992 .

[19]  Mahesh Gupta,et al.  A genetic algorithm-based approach to cell composition and layout design problems , 1996 .

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[21]  David B. Fogel,et al.  Schema processing under proportional selection in the presence of random effects , 1997, IEEE Trans. Evol. Comput..