Abstract
A number of companies have reaped the benefits offered by cellular manufacturing systems (CMSs) (Wemmerlov and Hyer, 1986). Typical benefits include: set-up time reduction, improved machine utilization, an increase in production rate and employee morale, a decrease in labor costs and traffic congestion and better control of the manufacturing system. Cellular manufacturing (CM) involves grouping of machines based on the parts they manufacture. The grouping of machines and parts in a CMS is briefly explained here by considering a simple example. Figure 9.1 shows four part routings. The first row indicates that part 1 is routed through machines A, C and E in succession. Figure 9.2 shows the arrangement of the machines referred to in Fig. 9.1 in a job shop environment. Figure 9.3 shows the arrangement of the same machines in ‘cells’. Notice that each cell is dedicated to production of a set of parts. The main idea behind grouping ‘like’ machines into cells is to minimize intercellular part movement. After grouping is done, the next logical step is to develop a layout for the cells and machines within each cell so that both intercellular and intracellular material handling cost is minimized. This step, however, is often ignored.
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References
Askin, R.G. and Chiu, K.S., (1990) A graph partitioning procedure for machine assignment and cell formation in group technology. International Journal of Production Research, 28,(8), 1555–72.
Ballakur, A. and Steudel, H.J. (1987) Within-cell utilization based heuristic for designing cellular manufacturing system. International Journal of Production Research, 25 (5), 639–65.
Boe, W.J. and Cheng, C.H. (1991) A close neighbor algorithm for designing cellular manufacturing systems. International Journal of Production Research, 29, (10) 2097–16.
Burbidge, J.L. (1963) Production flow analysis. The Production Engineer, 42 (12), 742.
Burbidge, J.L. (1977) A manual method of production flow analysis. The Production Engineer, 56, (10) 34–38.
Carpenter, G.A. and Grossberg, S. [1788] ART of adaptive pattern recognition by self-organizing neural network. Computer 21, (3) 77–88.
Chan, H.M. and Milner, D.A. (1982) Direct clustering algorithm for group formation in cellular manufacture. Journal of Manufacturing Systems, 1(1), 65–75.
Chandrasekharan, M.P. and Rajagopalan, R. (1986) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. International Journal of Production Research, 24(2), 451–64.
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), 835–50.
De Beer, C. and De Witte, J. (1978) Production flow synthesis. CIRP Annalen, 27, 389–92.
De Beer, C, Van Gerwen, R. and De Witte, J. (1976) Analysis of engineering production systems as a base for production-oriented reconstruction. CIRP Annalen, 25, 439–41.
De Witte, J. (1980) The use of similarity coefficients in production flow analysis International Journal of Production Research, 18(4) 503–14.
Heragu, S.S. (1992) Recent models and techniques for the layout problem, European Journal of Operational Research, 57(2), 136–44.
Heragu, S.S. (1994) Group technology and cellular manufacturing. IEEE Transactions on Systems, Man, and Cybernetics, SMC-24(2) 203–15.
Heragu, S.S. and Alfa, A.S. (1990) A simulated annealing based approach to solve the facility layout problem, in Proceedings of Fourth Advanced Technology Conference, Washington DC, November 5–7, 489–99.
Heragu, S.S. and Alfa, A.S. (1992) Experimental analysis of simulated annealing based algorithm for the layout problem European Journal of Operational Research, 57(2), 190–202.
Heragu, S.S. and Gupta, Y.P. (1994) A heuristic for designing cellular manufacturing facilities. International Journal of Production Research, 32(1), 125–40.
Heragu, S.S. and Kakuturi, S.R. (1992) Grouping and placement of machine cells. DSES Technical Report 37–92–341, Rensselaer Polytechnic Institute, Troy, NY.
Iri, M. (1968) On the synthesis of loop and cutset matrices and related problems. RAAG Memoirs, 4(A-XII), 376–410.
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), 1353–68.
King, J.R. (1980) Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. International Journal of Production Research, 18(2), 213–32.
King, J.R. and Nakornchai, V. (1982) Machine-component group formation in group technology: review and extension. International Journal of Production Research, 20(2), 117–33.
Kusiak, A. and Chow, W.S. (1987) An efficient cluster identification algorith, IEEE Transactions on Systems, Man, and Cybernetics, SMC-17(4), 696–99.
Logendran, R. (1990) A workload based model for minimizing total intercell and intracell moves in cellular manufacturing. International Journal of Production Research, 28(5), 913–25.
McAuley, J., (1972) Machine grouping for efficient production, The Production Engineer, 51(2), 53–57.
McCormick, W.T., Schweitzer, P.J. and White, T.E. (1972) Problem decomposition and data reorganization by a clustering technique. Operations Research, 20, 993–1009.
Muther, R. (1973) Systematic Layout Planning, Cahners Books, Boston, MA.
Nagi, R., Harhalakis, G. and Proth, J. (1990) Multiple routings and capacity considerations in group technology applications. International Journal of Production Research, 28(12), 2243–57.
Rajagopalan, R. and Barra, J.L. (1975) Design of cellular production systems — a graph theoretic approach. International Journal of Production Research, 13(6), 567–79.
Rajamani, D., Singh, N. and Aneja, Y.P. (1992) A model for cell formation in manufacturing systems with sequence dependence. International Journal of Production Research, 30(6), 1227–35.
Seifoddini, H. and Wolfe, P.M. (1986) Application of the similarity coefficient method in group technology. HE Transactions, 22(1), 271–77.
Srinivasan, G. and Narendran, T.T. (1991) GRAPHICS — a nonhierarchical clustering algorithm for group technology. International Journal of Production Research, 29(3), 463–78.
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), 145–52.
Teng, S. and Black, J.T., (1990) Cellular manufacturing systems modeling: the petri net approach. Journal of Manufacturing Systems, 9(1), 45–54.
Vakharia, A.J. and Wemmerlov, U. (1990) Designing a cellular manufacturing system: a materials flow based operations sequence. HE Transactions, 22(1), 84–97.
Vohra, T., Chen, D., Chang, J.C. and Chen, H. (1990) A network approach to cell formation in cellular manufacturing. International Journal of Production Research, 28(11), 2075–84.
Wemmerlov, U. and Hyer, N.L. (1986) Procedures for the part family/machine group identification problem in cellular manufacturing. Journal of Operations Management, 6(2), 125–147.
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© 1997 Chapman & Hall
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Heragu, S.S., Kakuturi, S.R. (1997). An interactive program for machine grouping and layout. In: Parsaei, H.R., Kolli, S., Hanley, T.R. (eds) Manufacturing Decision Support Systems. Manufacturing Systems Engineering Series, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1189-8_9
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DOI: https://doi.org/10.1007/978-1-4613-1189-8_9
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