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Part family formation by memory association

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Abstract

Most of the part family formation methods are concerned with “how to form” the families as opposed to “how to identify” the families. However, a more appropriate approach would be to identify “naturally occurring” families since these methods are based on the production flow analysis, which uses already implemented routing data. This paper presents a new approach using the memory association of neural networks to identify naturally existing families. The developed system, Feature-Based Memory Association Network (FBMAN), operates by the exhaustive association approach which deals with the difficult problem of exceptional parts. Comparison with the results generated by other methods proves the effectiveness of FBMAN.

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References

  1. W. T. McCormick et al., “Problem decomposition and data reorganization by a clustering technique”,Operations Research 20(5), pp. 993–1009, 1972.

    Google Scholar 

  2. J. R. King, “Machine component grouping in production flow analysis: an approach using a rank order clustering algorithm”,International Journal of Production Research 18(2), pp. 213–232, 1980.

    Google Scholar 

  3. M. P. Chandrasekharan and R. Rajagopalan, “MODROC: an extension of rank order clustering for group technology”,International Journal of Production Research 24(5), pp. 1221–1233, 1986.

    Google Scholar 

  4. H. M. Chan and D. A. Milner, “Direct clustering algorithm for group formation in cellular manufacture”,Journal of Manufacturing Systems,1, pp. 64–76, 1982.

    Google Scholar 

  5. J. Miltenburg and A. R. Montazemi, “Revisiting the cell formation problem: assigning parts to production systems”,International Journal of Production Research,31(11), pp. 2727–2746, 1993.

    Google Scholar 

  6. J. McAuley, “Machine grouping for efficient production”,Production Engineer 51, pp. 53–57, 1972.

    Google Scholar 

  7. H. Seifoddini and M. Djassemi, “The production data-based similarity coefficient versus jaccard's similarity coefficient”,Computer and Industrial Engineering 21(1–4), pp. 263–266, 1991.

    Google Scholar 

  8. H. K. Seifoddini and C. Hsu, “Comparative study of similarity coefficients and clustering algorithms in cellular manufacturing”,Journal of Manufacturing Systems,13(2), pp. 119–127, 1994.

    Google Scholar 

  9. C. T. Mosier, “An experiment investigating the application of clustering procedures and similarity coefficients to the GT machine cell formation problem”,International Journal of Production Research 27(10), pp. 1811–1835, 1989.

    Google Scholar 

  10. G. F. K. Purcheck, “A mathematical classification as a basis for the design of group technology production cells”,Production Engineering 54(1), pp. 35–48, 1974.

    Google Scholar 

  11. A. Kusiak, “The generalized group technology concept”,International Journal of Production Research 25(4), pp. 561–569, 1987.

    Google Scholar 

  12. G. Harhalakis, G. Ioannou, I. Minis and R. Nagi, “Manufacturing cell formation under random product demand”,International Journal of Production Research 32(1), pp. 47–64, 1994.

    Google Scholar 

  13. A. Atmani, R. S. Lashkari and R. J. Caron, “A mathematical programming approach to joint cell formation and operation allocation in cellular manufacturing”,International Journal of Production Research 33(1), pp. 1–15, 1995.

    Google Scholar 

  14. J. Peklenik and J. Grum, “Investigation of the computer aided classification of parts”,Annals of the CIRP 29(1), pp. 319–323, 1980.

    Google Scholar 

  15. H. L. Wu, R. Venugopal and M. M. Barash, “Design of a cellular manufacturing system: A synaptic pattern recognition approach”,Journal of Manufacturing Systems,5, pp. 81–88, 1986.

    Google Scholar 

  16. H. Xu and H. P. Wang, “Part family formation for GT applications based on fuzzy mathematics”,International Journal of Production Research 27(9), pp. 1637–1651, 1989.

    Google Scholar 

  17. A. Kusiak, “EXGT-S: a knowledge based system for group technology”,International Journal of Production Research 26(5), pp. 887–904, 1988.

    Google Scholar 

  18. V. Venugopal and T. T. Narendran, “Neural network model for design retrieval in manufacturing systems”,Computers in Industry 20(1), pp. 11–23, 1992.

    Google Scholar 

  19. S. C-Y. Lu, “Machine learning techniques for group technology applications”,CIRP Annals 38, pp. 445–459, 1989.

    Google Scholar 

  20. Y. B. Moon, “Forming part-machine families for cellular manufacturing: a neural network approach”,International Journal of Advanced Manufacturing Technology,5(4), pp. 278–291, 1990.

    Google Scholar 

  21. C. H. Dagli and R. Huggahalli, “Machine-part family formation with the adaptive resonance theory paradigm”,International Journal of Production Research 33(4), pp. 893–913, 1995.

    Google Scholar 

  22. H. A. Rao and P. Gu, “A multi-constraint neural network for the pragmatic design of cellular manufacturing systems”,International Journal of Production Research 33(4), pp. 1049–1070, 1995.

    Google Scholar 

  23. Y. B. Moon and S. C. Chi, “Generalized part family formation using neural network techniques”,Journal of Manufacturing Systems 11(3), pp. 149–159, 1992.

    Google Scholar 

  24. P. H. Waghodekar and S. Sahu, “Machine-component cell formation in group technology: MACE”,International Journal of Production Research 22(6), pp. 937–948, 1984.

    Google Scholar 

  25. G. Srinivasan and T. T. Narendran, “GRAFICS — a nonhierarchical clustering algorithm for group technology”,International Journal of Production Research 29(3), pp. 463–478, 1991.

    Google Scholar 

  26. K. R. Kumar, A. Kusiak and A. Vannelli, “Grouping of parts and components in flexible manufacturing systems”,European Journal of Operational Research 24, pp. 387–397, 1986.

    Google Scholar 

  27. G. Srinivasan, T. T. Narendran and B. Mahadevan, “An assignment model for the part-families problem in Group technology”,International Journal of Production Research 28(1), pp. 145–152, 1990.

    Google Scholar 

  28. H. K. Seifoddini, “Single linkage versus average linkage clustering in machine cells formation applications”,Computers and Industrial Engineering 16(3), pp. 419–426, 1989.

    Google Scholar 

  29. A. Ballakur and H. J. Steudel, “A within-cell utilization based heuristic for designing cellular manufacturing systems”,International Journal of Production Research 25(5), pp. 639–665, 1987.

    Google Scholar 

  30. A. S. Carrie, “Numerical taxonomy applied to group technology and plant layout”,International Journal of Production Research 11, pp. 399–416, 1973.

    Google Scholar 

  31. J. A. Ventura, F. F. Chen and C. H. Wu, “Grouping parts and tools in flexible manufacturing systems production planning”,International Journal of Production Research 28(6), pp. 1039–1056, 1990.

    Google Scholar 

  32. R. G. Askin and S. P. Subramanian, “A cost-based heuristic for group technology configuration”,International Journal of Production Research 25(1), pp. 101–113, 1987.

    Google Scholar 

  33. K. R. Kumar and A. Vannelli, “Strategic subcontracting for efficient disaggregated manufacturing”,International Journal of Production Research 25(12), pp. 1715–1728, 1987.

    Google Scholar 

  34. M. P. Chandrasekharan and R. Rajagopalan, “GROUPABILITY: an analysis of the properties of binary data matrices for group technology”,International Journal of Production Research 27(6), pp. 1035–1052, 1989.

    Google Scholar 

  35. M. P. Chandrasekharan and R. Rajagopalan, “ZODIAC: an algorithm for concurrent formation of part-families and machine-cells”,International Journal of Production Research 25(6), pp. 835–850, 1987.

    Google Scholar 

  36. C. H. Chu and M. Tsai, “A comparison of three array-based clustering techniques for manufacturing cell formation”,International Journal of Production Research 28(8), pp. 1417–1433, 1990.

    Google Scholar 

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Authors and Affiliations

  1. Manufacturing Intelligence Laboratory, Department of Mechanical, Aerospace and Manufacturing Engineering, Syracuse University, Syracuse, USA

    Y. Kao &  Y. B. Moon

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  1. Y. Kao
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  2. Y. B. Moon
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Kao, Y., Moon, Y.B. Part family formation by memory association. Int J Adv Manuf Technol 13, 649–657 (1997). https://doi.org/10.1007/BF01350823

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