Multi-Objective Cellular Manufacturing System under Machines with Different Life-Cycle using Genetic Algorithm
In this paper a multi-objective nonlinear programming
model of cellular manufacturing system is presented which minimize
the intercell movements and maximize the sum of reliability of cells.
We present a genetic approach for finding efficient solutions to the
problem of cell formation for products having multiple routings.
These methods find the non-dominated solutions and according to
decision makers prefer, the best solution will be chosen.
[1] Boctor, F.F., The minimum cost machine-part cell formation problem.
Int. J. Prod. Res., 1996, 34, 1045-1063.
[2] Burbidge, J.L., The Introduction of Group Technology, 1975 (Wiley:
New York).
[3] Dimopoulos, C. and Mort, N., A hierarchical clustering methodology
based on genetic programming for the solution of simple cell-formation
problems. Int. J. Prod. Res., 2001, 39, 1-19.
[4] Dimopoulos, C. and Mort, N., Evolving knowledge for the solution of
clustering problems in cellular manufacturing. Int. J. Prod. Res., 2004,
42, 4119-4133.
[5] Goldberg, D., Genetic Algorithm in search, optimization and machine
learning, Addison-Wesley, Reading, MA, 1989.
[6] Grefenstette, J., editor, Proceedings of the Second International
Conference on Genetic Algorithms. Lawrence Erlbaum Associates,
Hillsdale, NJ, 1987.
[7] Ho. Y.-C. and Moodie, C.L., Solving cell formation problems in a
manufacturing environment with flexible processing and routing
capabilities. Int. J. Prod. Res., 1996, 34, 2901-2923.
[8] Kusiak, A., Intelligent Manufacturing Systems, 1990 (Prentice Hall:
Englewood Cliffs, NJ)
[9] Lee, H. and Garcia-Diaz, A., A network flow approach to solve
clustering problems. Int. J. Prod. Res., 1993, 31, 603-612.
[10] Mansouri, S.A., Moattar Husseini, S.M. and Newman, S.T., A review of
the modern approaches to multi-criteria cell design. Int. J. Prod. Res.,
2000, 38, 1201-1218.
[11] Selim, M.H., Askin, R.G. and Vakharia, A.J., Cell formation in group
technology: review, evaluation and directions for future research.
Comput. Ind. Engng., 1998, 34, 3-20.
[12] Seifoddini, S., Djassemi, M., 2001. The effect of reliability
consideration on the application of quality index. Computers and
Industrial Engineering 40 (1-2), 65-77.
[13] Wei, J.C. and Gaither, N., An optimal model for cell-formation
decisions. Decis. Sci., 1990, 21, 416-433.
[14] Wemmerlov, U. and Johnson, D.J., Empirical findings in
manufacturing cell design. Int. J.Prod. Res., 2000, 38, 481-507.
[15] Zitzler, E. and Thiele, L., Multiobjective evolutionary algorithms: a
comparative study and the Strength Pareto approach. IEEE Trans. Evol.
Comput., 1999, 3, 257-271.
[1] Boctor, F.F., The minimum cost machine-part cell formation problem.
Int. J. Prod. Res., 1996, 34, 1045-1063.
[2] Burbidge, J.L., The Introduction of Group Technology, 1975 (Wiley:
New York).
[3] Dimopoulos, C. and Mort, N., A hierarchical clustering methodology
based on genetic programming for the solution of simple cell-formation
problems. Int. J. Prod. Res., 2001, 39, 1-19.
[4] Dimopoulos, C. and Mort, N., Evolving knowledge for the solution of
clustering problems in cellular manufacturing. Int. J. Prod. Res., 2004,
42, 4119-4133.
[5] Goldberg, D., Genetic Algorithm in search, optimization and machine
learning, Addison-Wesley, Reading, MA, 1989.
[6] Grefenstette, J., editor, Proceedings of the Second International
Conference on Genetic Algorithms. Lawrence Erlbaum Associates,
Hillsdale, NJ, 1987.
[7] Ho. Y.-C. and Moodie, C.L., Solving cell formation problems in a
manufacturing environment with flexible processing and routing
capabilities. Int. J. Prod. Res., 1996, 34, 2901-2923.
[8] Kusiak, A., Intelligent Manufacturing Systems, 1990 (Prentice Hall:
Englewood Cliffs, NJ)
[9] Lee, H. and Garcia-Diaz, A., A network flow approach to solve
clustering problems. Int. J. Prod. Res., 1993, 31, 603-612.
[10] Mansouri, S.A., Moattar Husseini, S.M. and Newman, S.T., A review of
the modern approaches to multi-criteria cell design. Int. J. Prod. Res.,
2000, 38, 1201-1218.
[11] Selim, M.H., Askin, R.G. and Vakharia, A.J., Cell formation in group
technology: review, evaluation and directions for future research.
Comput. Ind. Engng., 1998, 34, 3-20.
[12] Seifoddini, S., Djassemi, M., 2001. The effect of reliability
consideration on the application of quality index. Computers and
Industrial Engineering 40 (1-2), 65-77.
[13] Wei, J.C. and Gaither, N., An optimal model for cell-formation
decisions. Decis. Sci., 1990, 21, 416-433.
[14] Wemmerlov, U. and Johnson, D.J., Empirical findings in
manufacturing cell design. Int. J.Prod. Res., 2000, 38, 481-507.
[15] Zitzler, E. and Thiele, L., Multiobjective evolutionary algorithms: a
comparative study and the Strength Pareto approach. IEEE Trans. Evol.
Comput., 1999, 3, 257-271.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52511", author = "N. Javadian and J. Rezaeian and Y. Maali", title = "Multi-Objective Cellular Manufacturing System under Machines with Different Life-Cycle using Genetic Algorithm", abstract = "In this paper a multi-objective nonlinear programming
model of cellular manufacturing system is presented which minimize
the intercell movements and maximize the sum of reliability of cells.
We present a genetic approach for finding efficient solutions to the
problem of cell formation for products having multiple routings.
These methods find the non-dominated solutions and according to
decision makers prefer, the best solution will be chosen.", keywords = "Cellular Manufacturing, Genetic Algorithm, Multiobjective,
Life-Cycle.", volume = "1", number = "6", pages = "288-5", }
