Abstract
In this paper, the problem of clustering machines into cells and components into part-families with the consideration of ratio-level and ordinal-level data is dealt with. The ratio-level data is characterized by the use of workload information obtained both from per-unitprocess times and production quantity of components, and from machine capacity. In the case of ordinal-level data, we consider the sequence of operations for every component. These data sets are used in place of conventional binary data for arriving at clusters of cells and part-families. We propose a new approach to cell formation by viewing machines, and subsequently components, as 'points' in multi-dimensional space, with their coordinates defined by the corresponding elements in a Machine-Component Incidence Matrix (MCIM). An iterative algorithm that improves upon the seed solution is developed. The seed solution is obtained by formulating the given clustering problem as a Traveling Salesman Problem (TSP). The solutions yielded by the proposed clustering algorithm are found to be good and comparable to those reported in the literature.
Similar content being viewed by others
References
Arvindh B, Irani SA, (1994) Principal component analysis for evaluating the feasibility of cellular manufacturing without initial machine-part clustering. International Journal of Production Research 32:1909–1938
Chandrasekaran MP, Rajagopalan R, (1987) ZODIAC: an algorithm for concurrent formation of part-families and machine-cells. International Journal of Production Research 25:835–850
Chu CH, (1989) Cluster analysis in manufacturing cell formation. OMEGA 17:289–295
Kandiller L, (1994) A comparative study of cell formation in cellular manufacturing systems. International Journal of Production Research 32:2395–2429
King JR, Nakornchai V, (1982) Machine component group formation in group technology-review and extension. International Journal of Production Research 20:117–133
Kusiak A, (1987) The generalised group technology concept. International Journal of Production Research 25:561–569
Kusiak A, and Chow WS, (1987) Efficient solving of the group technology problem. Journal of Manufacturing Systems 6:117–124
Luong LHS, (1993) A cellular similarity coefficient algorithm for the design of manufacturing cells. International Journal of Production Research 31:1757–1766
McAuley J, (1972) Machine grouping for efficient production. Production Engineer 51:53–57
Mosier CT, (1989) An experiment investigating the application of clustering procedures and similarity coefficient to the GT machine cell formation problem. International Journal of Production Research 27:1811–1835
Nair JG, Narendran TT, (1998) CASE: A clustering algorithm for cell formation with sequence data. International Journal of Production Research 36:157–179
Nair JG, Narendran TT, (1999) ACCORD: A bicriterion algorithm for cell formation using ordinal and ratio-level data. International Journal of Production Research 37:539–556
Offodile OF, Mehrez A, Grznar J, (1994) Cellular manufacturing: a taxonomic review framework. Journal of Manufacturing Systems 13:196–220
Purcheck GFK, (1975) A mathematical classification as a basis of design of group technology production cells. Production Engineer 54:35–48
Rajagopalan R, Batra JL, (1975) Design of cellular production systems: a graph theoretic approach. International Journal of Production Research 13:567–579
Sarkar BR, Xu Y, (2000) Designing multi-product lines: job routing in cellular manufacturing systems. IIE Transactions 32:219–235
Seifoddini H, Wolfe P M, (1986) Application of similarity coefficient method in GT. IIE Transactions 21:382–388
Singh N, (1993) Design of cellular manufacturing systems. European Journal of Operational Research 69:284–291
Srinivasan G, (1994) A clustering algorithm for machine cell formation in group technology using minimum spanning trees. International Journal of Production Research 32:2149–2158
Srinivasan G, Narendran TT, (1991) GRAFICS: a nonhierarchical clustering algorithm for group technology. International Journal of Production Research 29:463–478
Srinivasan G, Narendran TT, Mahadevan M, (1990) An assignment model for the part-families problem in group technology. International Journal of Production Research 29:463–478
Steudel HJ, Ballakur A, (1987) A dynamic programming based heuristic for machine grouping in manufacturing cell formation. Computers & Industrial Engineering 12:215–222
Venugopal V, (1999) Soft-computing-based approaches to the group technology problem: a state-of-the-art review. International Journal of Production Research 37:3335–3357
Venugopal V, and Narendran TT, (1992a) Cell formation in cellular manufacturing systems through simulated annealing. European Journal of Operational Research 63:409–422
Venugopal V, Narendran TT, (1992b) Design of cellular manufacturing systems based on asymptotic forms of a Boolean matrix. European Journal of Operational Research 67:405–417
Venugopal V, Narendran TT, (1992c) A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Computers & Industrial Engineering 22:469–480
Viswanathan, (1995) Configuring cellular manufacturing systems: a quadratic integer programming and a simple interchange heuristic. International Journal of Production Research 33:361–376
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
George, A.P., Rajendran, C. & Ghosh, S. An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data. Int J Adv Manuf Technol 22, 125–133 (2003). https://doi.org/10.1007/s00170-002-1451-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-002-1451-7