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Solving a dynamic cell formation problem using metaheuristics. (English) Zbl 1104.92018
Summary: Solving a cell formation (CF) problem in dynamic conditions is going to be discussed by using some traditional metaheuristic methods such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS). Most of previous researches were done under the static condition. Due to the fact that CF is an NP-hard problem, solving the model using classical optimization methods needs a long computational time. In this research, a nonlinear integer model of CF is first given and then solved by GA, SA and TS. Then, the results are compared with the optimal solution and the efficiency of the proposed algorithms is discussed.

92C37Cell biology
90C59Approximation methods and heuristics
Full Text: DOI
[1] Montreuil, B.; Laforge, A.: Dynamic layout design given a scenario tree of probable futures. European journal of operational research. 63, 271-286 (1992)
[2] Wilhelm, W.; Chiou, C.; Chang, D.: Integrating design and planning considerations in cell formation. Annals of operations research 77, No. 1, 97-107 (1998) · Zbl 0897.90117
[3] Benjaafar, S.; Sheikhzadeh, M.: Design of flexible plant layouts. IIE transactions 32, No. 4, 309-322 (2000)
[4] Yang, T.; Peters, B.: Flexible machine layout design for dynamic and uncertain production conditions. European journal of operational research 108, 49-64 (1998) · Zbl 0943.90025
[5] Chen, M.: A mathematical programming model for systems reconfiguration in a dynamic cell formation condition. Annals of operations research 77, No. 1, 109-128 (1998) · Zbl 0897.90106
[6] Song, S.; Hitomi, K.: Integrating the production planning and cellular, layout for flexible cell formation. Production planning and control 7, No. 6, 585-593 (1996)
[7] Seifoddini, H.: A probabilistic model for machine cell formation. Journal of manufacturing systems 9, No. 1, 69-75 (1990)
[8] Harahalaks, G.; Nagi, R.; Proth, J.: An efficient heuristic in manufacturing cell formation to group technology applications. International journal of production research 28, No. 1, 185-198 (1990)
[9] A. Mungwatanna, Design of cellular manufacturing systems for dynamic and uncertain production requirement with presence of routing flexibility, Ph.D. Thesis, Blacksburg State University Virginia, 2000.
[10] Holland, J. H.: Adaptation in natural and artificial systems. (1975) · Zbl 0317.68006
[11] Goldberg, D. E.: Genetic algorithm in search optimization, and machine learning. (1989) · Zbl 0721.68056
[12] J.D. Bagley, The behavior of adaptive systems which employ genetic and correlation algorithms, Ph.D. Thesis, University of Michigan, 1976.
[13] R.S. Rosenberg, Simulation of genetic population with biochemical properties, Ph.D. Thesis, University of Michigan, 1967.
[14] D.J. Cavicchio, Adaptive search using simulated evolution, Ph.D. Thesis, University of Michigan, 1972.
[15] Gupta, Y.; Gupta, M.; Kumar, A.; Sundram, C.: Minimizing total inter-cell and intra-cell moves in cell formation: a genetic algorithm. International journal of computer integrated manufacturing 8, No. 2, 92-101 (1995)
[16] Joines, J.; Culberth, C.; King, R.: Manufacturing cell design: an integer programming model employing genetic algorithms. IIE transactions 28, No. 1, 69-85 (1996)
[17] Venugopal, V.; Narendran, T.: A genetic algorithm approach to the machine component grouping problem with multiple objectives. Computers and industrial engineering 22, No. 4, 469-480 (1992)
[18] Kirkpatrick, F.; Gelatt, C.; Vecci, M.: Optimization by simulated annealing. Science 220, 671-680 (1983) · Zbl 1225.90162
[19] Metropolis, N.; Rosenbluth, A.; Rosenbluth, M.; Teller, A.: Equations of state calculations by fast computing machines. Journal of chemical physics 21, 1087-1092 (1953)
[20] Boctor, F. F.: A linear formulation of the machine-part cell formation problem. International journal of production research 29, No. 2, 343-356 (1991)
[21] Sofanopoulou, S.: Manufacturing cells design with alternative process plans and/or replicate machines. International journal of production research 37, No. 3, 707-720 (1990)
[22] Vakharia, A.; Chang, Y.: Cell formation in group technology: a combinatorial search approach. International journal of production research 35, No. 7, 84-97 (1997) · Zbl 0940.90518
[23] Logendran, R.; Ramakrishna, P.; Srikandarajah, C.: Tabu search based heuristic for cellular manufacturing systems in the presence of alternative process plans. International journal of production research 32, No. 2, 273-297 (1994) · Zbl 0911.90187