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Cell Formation Considering the Minimization of Manufacturing Leadtime in Cellular Manufacturing Systems  

Yim, Dong-Soon (Department of Industrial and Systems Engineering, Hannam University)
Woo, Hoon-Shik (Department of Internet and Information Engineering, Daejon University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.30, no.4, 2004 , pp. 285-293 More about this Journal
Abstract
In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.
Keywords
cellular manufacturing systems; group technology; genetic algorithm;
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1 Davis, J. (1985), Applying Adaptive Algorithms to Epistatic Domains, Proceedings of the International Joint Conference on Artificial Intelligence, 162-164
2 Goldberg, D. E. andLingle, R. (1985), Alleles, Loci, and the TSP, Proceedings of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, NJ, 154-159
3 Jones, D. R. and Bertramo, M. A. (1991), Solving Partitioning Problems with Genetic Algorithms, Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 442-449
4 Gupta, Y. P., Gupta, M. C., Kumar, A, Sundrum, C. (1995), Minimizing total intercell and intracell moves in cellular manufacturing: A genetic algorithm approach, International Journal of Computer Integrated Manufacturing, 6(2), 92-101
5 Plaquin, M. and Pierreval, H. (2000), Cell formation using evolutionary algorithms with certain constraints, International Journal of Production Economics, 64, 267-278
6 Harhalakis, G., Proth, J. M.,Xie, X. J. (1990), Manufacturing cell design using simulated annealing: An industrial application, Journal of Intelligent Manufacturing, 1, 185-191
7 MacQueen, J. (1967), Some Methods for Classification and Analysis of Multivariate Observations, Proceedings on 5th Berkeley Symposium, 281-297
8 Bhuyan, J. N, Raghavan, V. V., and Elayavalli, V. K. (1991), Genetic Algorithm for Clustering with an Ordered Representation, Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 408-415
9 Oliver, J. M.,Smith, D.J.,and Holland, J.R. C.(1987), A Study of Permutation Crossover Operators on the traveling Salesman Problem, Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, NJ, 224-230
10 Zolfaghari. S. and Liang, M. (2002), Comparative study of simulated annealing, genetic algorithms and tabu search for solving binary and comprehensive machine-grouping problems, International Journal of Production Research, 40(9), 2141-2158   DOI   ScienceOn
11 Groover, M.P. and Zimmers, E. W. (1984), CAD/CAM: Computer Aided Design and Manufacturing, Englewood Cliffs: Prentice Hall
12 Kaufman, J. and Rousseeuw, P. J. (1990), Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, Inc