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Design of cellular manufacturing systems using objective functional clustering algorithms

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Abstract

Cellular manufacturing (CM) has emerged as an alternative to conventional batch-type manufacturing owing to the former's capability of reducing set-up times, in-process inventories and throughput times. It provides the basis for implementation of just-in-time (JIT) and flexible manufacturing systems (FMS). The machine-part group formation is an important issue in the design of CMSs. This paper presents objective functional clustering algorithms for cell formation problems in the design of cellular manufacturing systems. A deterministic objective functional algorithm (hard clustering) and a fuzzy objective functional algorithm (fuzzy clustering) are used to form the part families and machine cells simultaneously. A collection of data sets from open literature is used to test these algorithms. A software package has been developed to verify the implementation.

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References

  1. A. Kusiak and W. S. Chow, “Efficient solving of the group technology problem”,Journal of Manufacturing Systems,6(2), pp. 1081–1100, 1987.

    Google Scholar 

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

    Google Scholar 

  3. R. J. Schonberger,Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity. The Free Press, New York, 1982.

    Google Scholar 

  4. F. F. Boctor, “A linear formation of the machine-part cell formation problem”,International Journal of Production Research,29, pp. 343–356, 1991.

    Google Scholar 

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

    Google Scholar 

  6. C. J. McCormick, D. W. Sweitzer and T. R. White, “Problem decomposition and data reorganisation by a clustering technique”,Operations Research,20, pp. 993–1009, 1972.

    Google Scholar 

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

    Google Scholar 

  8. J. R. King and V. Nakornchai, “Machine component group formation in group technology-review and extension”,International Journal of Production Research,20, pp. 117–133, 1982.

    Google Scholar 

  9. M. P. Chandrasekaran and R. Rajagopalan, “MODROC-An extension of rank order clustering for group technology”,International Journal of Production Research,24, pp. 1221–1233, 1986.

    Google Scholar 

  10. H. M. Chan and D. A. Milner, “Direct clustering algorithm for group formation in cellular manufacture”,Journal of Manufacturing Systems,1, pp. 65–74, 1982.

    Google Scholar 

  11. H. C. Co and A. Arrar, “Configuring cellular manufacturing systems”,International Journal of Production Research,26, pp. 1511–1522, 1988.

    Google Scholar 

  12. A. Kusiak, “The part families problem in flexible manufacturing systems”,Annals of Operations Research,3, pp. 279–300, 1985.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  16. C. Mosier and L. Taube, “Weighted similarity measure heuristics for group technology clustering problem”,Omega,13, pp. 381–391, 1985.

    Google Scholar 

  17. A. Kusiak, “The generalised group technology concept”,International Journal of Production Research,25, pp. 561–569, 1987.

    Google Scholar 

  18. L. Stanfel, “Machine clustering for economic production”,Engineering Costs and Production Economics,9, pp. 73–81, 1985.

    Google Scholar 

  19. M. P. Chandrasekaran and R. Rajagopalan “An ideal seed nonhierarchical clustering algorithm for cellular manufacture”,International Journal of Production Research,24, pp. 451–464, 1986.

    Google Scholar 

  20. J. B. McQueen, “Some methods for classification and analysis of multivariate observations”, Proceedings of the Fifth Symposium on Mathematical Statistics and Probability, University of California, Berkeley, pp. 281–297, 1967.

  21. M. P. Chandrasekaran and R. Rajagopalan, “ZODIAC—an algorithm for concurrent formation of part-families and machine-cells”,International Journal of Production Research,25, pp. 835–850, 1987.

    Google Scholar 

  22. R. Rajagopalan and J. L. Batra, “Design of cellular production systems — a graph theoretic approach”,International Journal of Production Research,13, pp. 567–579, 1975.

    Google Scholar 

  23. A. Vannelli and K. R. Kumar, “A method for finding minimal bottleneck cells for grouping part-machine families,International Journal of Production Research,24, pp. 387–400, 1986.

    Google Scholar 

  24. K. R. Kumar and A. Vannelli, “Strategic subcontracting for efficient disintegrated manufacturing”,International Journal of Production Research,3, pp. 279–300, 1987.

    Google Scholar 

  25. F. Choobineh “A framework for the design of cellular manufacturing systems”,International Journal of Production Research,26, pp. 1161–1172, 1988.

    Google Scholar 

  26. K. Raja Gunasingh and R. S. Lashkari, “Machine grouping problem in cellular manufacturing systems — an integer programming approach”,International Journal of Production Research,27, pp. 14675, 1989.

    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, pp. 145–152, 1990.

    Google Scholar 

  28. H. J. Steudel and A. Ballakur, “A dynamic programming based heuristic for machine grouping in manufacturing cell formation”,Computers and Industrial Engineering,12, pp. 215–222, 1987.

    Google Scholar 

  29. J. C. Bezdeck,Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum Press, 1981.

    Google Scholar 

  30. M. R. Anderberg,Cluser Analysis for Application, New York, Academic Press, 1973.

    Google Scholar 

  31. A. J. Vakhria and U. Wemmerlov, “A new similarity index and clustering methodology for formation of manufacturing cells”,DSI Proceedings,2, pp. 1075–1077, 1988.

    Google Scholar 

  32. R. Panneerselvam and K. N. Balasubramanian, “Algorithmic grouping of operation sequences”,Engineering Costs and Production Economics,9, pp. 125–135, 1985.

    Google Scholar 

  33. C.-H. Chu and J. C. Hayya, “A fuzzy clustering approach to manufacturing cell formation”,International Journal of Production Research,29, pp. 1475–1487, 1991.

    Google Scholar 

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

    Google Scholar 

  35. C. Mosier and L. Taube, “The facets of group technology and their impacts on implementation — a state of the art survey”,Omega,17, pp. 289–295, 1985.

    Google Scholar 

  36. H. Seiffidini, “Single linkage vs average linkage clustering in machine cells formation applications”,Computers and Industrial Engineering,16, pp. 419–426, 1989.

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical and Production Engineering, PSG College of Technology, 641 004, Coimbatore, India

    S. G. Ponnambalam &  P. Aravindan

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  1. S. G. Ponnambalam
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  2. P. Aravindan
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Ponnambalam, S.G., Aravindan, P. Design of cellular manufacturing systems using objective functional clustering algorithms. Int J Adv Manuf Technol 9, 390–397 (1994). https://doi.org/10.1007/BF01748484

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