Machine-Part Grouping Formation Using Grid Computing

그리드 컴퓨팅을 이용한 기계-부품 그룹 형성

  • Lee, Jong-Sub (Department of Internet Science Technology, Woosong University) ;
  • Kang, Maing-Kyu (Department of Industrial Engineering, Hanyang University)
  • 이종섭 (우송대학교 IT(경영정보)학과) ;
  • 강맹규 (한양대학교 산업공학과)
  • Published : 2004.09.30

Abstract

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells using grid computing. It forms machine cells from the machine-part incidence matrix by means of Self-Organizing Maps(SOM) whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the machine vectors. It generates machine-part group which are assigned to machine cells by means of the number of bottleneck machine with processing part. The proposed algorithm was tested on well-known machine-part grouping problems. The results of this computational study demonstrate the superiority of the proposed algorithm.

Keywords

References

  1. Arvindi, B. and Irani, S. A.(1994), Principle component analysis for evaluating the feasibility of cellular manufacturing without initial machine-part matrix clustering, International Journal of Production Research, 32(8), 1909-1938 https://doi.org/10.1080/00207549408957050
  2. Ben Arieh, D. and Chang, P. T.(1994), An extension to p-median group technology algorithm, Computers andOperations Research, 21(1), 101-114 https://doi.org/10.1016/0305-0548(94)90065-5
  3. Carpenter, G. A. and Grossberg, S.(1988), The ART of adaptive pattern recognition by a self-organizing neural network, IEEE Computer, 21(3), 77-88
  4. Carrie, A. S.(1973), Numerical taxonomy Applied to group technology and plant layout, International Journal of Production Research, 11(1), 399-416
  5. Chan, H.M.andMilner, D. A.(1982), Direct clustering algorithm for group formation in cellular manufacturing, Journal of Manufacturing Systems, 1(1),65-75 https://doi.org/10.1016/S0278-6125(82)80068-X
  6. Chandrasekharan, M. P. and Rajagopalan, R.(1986), MODROC: An extension of rank order clustering for group technology, International Journal of Production Research, 24(5),1221-1233 https://doi.org/10.1080/00207548608919798
  7. Chandrasekharan, M. P. and Rajagopalan, R.(1987), ZODIAC: An algorithm for concurrent formation of part-families and machine-cells, International Journal of Production Research, 25(6), 835-850 https://doi.org/10.1080/00207548708919880
  8. Chandrasekharan, M. P. and Rajagopalan, R.(1989), Groupability: An analysis of the properties of binary data matrices for group technology, International Journal of Production Research, 27(6), 1035-1052 https://doi.org/10.1080/00207548908942606
  9. Crama, Y. and Oosten, M.(1996), Models for machine-part grouping in cellular manufacturing, International Journal of Production Research, 34(6), 1693-1713 https://doi.org/10.1080/00207549608904991
  10. Gupta, Y. P., Gupta, M. C., Kumar, A., and Sundram, C.(1995), Minimizing total intercell and intracell moves in cellular manufacturing: A genetic algorithm approach, International Journal of Production Research, 8(2), 92-101
  11. Ham, I., Hitomi, K., and Yoshida, T.(1985), Group Technology: Production Methods in Manufacture, Kluwer-Nijhoff, Boston, MA
  12. Kaparthi, S. and Suresh, N. C.(1992), Machine-component cell formation in group technology: A neural network approach, International Journal of Production Research, 30(6), 1353-1367 https://doi.org/10.1080/00207549208942961
  13. Kaparthi, S., Suresh, N. C., and Cervany, R. P.(1993), Animproved neural network leader algorithm for part-machine grouping in group technology, European Journal of Operational Research, 69(3),342-356 https://doi.org/10.1016/0377-2217(93)90020-N
  14. King, J. R.(1980), Machine-component group formation in production flow analysis: An approach using a rank order clustering algorithm. International Journal of Production Research, 18(2), 213-232
  15. Kohonen, T.(1984), Self-organization and association memory, Springer, Berlin
  16. Kulkarni, U. R. and Kiang, M.Y.(1995), Dynamic grouping of parts in flexible manufacturing systems: A self organizing neural networks approach, European Journal of Operational Research, 84(2), 192-212
  17. Kumar, C. S. and Chandrasekharan, M. P.(1990), Grouping efficacy: A quantitative criterion for goodness of block diagonal forms of binary matrices in group technology, International Journal of Production Research, 28(2), 233-243 https://doi.org/10.1080/00207549008942706
  18. Kusiak, A.(1990), Intelligent Manufacturing Systems, Prentice Hall, Englewood Cliffs, New Jersey
  19. Kusiak, A. and Cho, M.(1992), Similarity coefficient algorithms for solving the group technology problem, International Journal of Production Research, 30(11), 2633-2646 https://doi.org/10.1080/00207549208948181
  20. Kusiak, A. and Chow, W. S.(1987), Efficient solving of the group technology problem, Journal of Manufacturing Systems, 6(2), 117-124 https://doi.org/10.1016/0278-6125(87)90035-5
  21. McAuley, J. (1972), Machine grouping for efficient production, The Production Engineer, 51(2), 53-57
  22. Sandbothe, R. A.(1998), Two observations on the grouping efficacy measure for goodness of block diagonal forms, International Journal of Production Research, 36(11), 3217-3222 https://doi.org/10.1080/002075498192373
  23. Sarker, B. R.(2001), Measure of grouping efficiency in cellular manufacturing systems, European Journal of Operational Research, 130(4),588-611
  24. Srinivasan, G., Narendran, T. T., and Mahadevan, B.(1990), An assignment model for the part families problem in group technology, International Journal of Production Research, 28(1), 145-152 https://doi.org/10.1080/00207549008942689
  25. Viswanathan, S.(1996), A new approach for solving the p-median problem in group technology, International Journal of Production Research, 34(10), 2691-2700 https://doi.org/10.1080/00207549608905053