Abstract
This chapter outlines an intelligent fuzzy multi-criteria decision-making (MCDM) model for appropriate selection of a flexible manufacturing system (FMS) in a conflicting criteria environment. A holistic methodology has been developed for finding out the “optimal FMS” from a set of candidate-FMSs. This method of trade-offs among various parameters, viz., design parameters, economic considerations, etc., affecting the FMS selection process in an MCDM environment. The proposed method calculates the global priority values (GP) for functional, design factors and other important attributes by an eigenvector method of a pair-wise comparison. These GPs are used as subjective factor measures (SFMs) in determining the selection index (SI). The proposed fuzzified methodology is equipped with the capability of determining changes in the FMS selection process that results from making changes in the parameters of the model. The model achieves balancing among criteria. Relationships among the degree of fuzziness, level-of-satisfaction and the SIs of the MCDM methodology guide decision makers under a tripartite fuzzy environment in selecting their choice of trading-off with a predetermined allowable fuzziness. The measurement of level-of-satisfaction during making the appropriate selection of FMS is carried out.
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References
Abdel-Malek, L., and Wolf, C., 1991, Evaluating flexibility of alternative FMS designs A comparative measure, International Journal of Production Economics, 23(1-3): 3-10.
Barad, M., and Sipper, D., 1988, Flexibility in manufacturing systems: definitions and petri net modeling, International Journal of Production Research, 26: 237-248.
Browne, J., Dubois, D., Rathmill, K., Sethi, S.P., and Stecke, K.E., 1984, Classification of flexible manufacturing systems, FMS Magazine, 2: 114-117.
Buffa, E.S., 1993, Modern Production/Operations Management, Wiley Eastern Limited, New Delhi.
Buzacott, J.A., and Mandelbaum, M., 1985, Flexibility and productivity in manufacturing systems, Proceedings of the Annual IIE Conference, Los Angeles, CA, pp. 404-413.
Chen, Y., Tseng M.M., and Yien, J., 1998, Economic view of CIM system architecture, Production Planning & Control, 9(3): 241-249.
Elango, B., and Meinhart, W.A., 1994, Selecting a flexible manufacturing system: a strategic approach. Long Range Planning, 27(3): 118-126.
Evans, G.W., and Brown, P.A., 1989, A multi objective approach to the design of flexible manufacturing systems, in Proceedings of International Industrial Engineering Conference on Manufacturing and Societies, pp. 301-305.
Gupta, Y.P., and Goyal, S., 1989, Flexibility of manufacturing systems: concepts and measurements, European Journal of Operational Research, 43: 119-135.
Gindy, N.N.Z., and Ratchev, S.M., 1998, Integrated framework for machining equipment in selection of CIM, International Journal of Computer Integrated Manufacturing, 11(4): 311-325.
Haddock, J., and Hartshorn, T.A., 1989, A decision support system for specific machine selection, Computers & Industrial Engineering, 16(2): 277-286.
Kaighobadi, M., and Venkatesh, 1994, Flexible manufacturing systems: an overview, International Journal of Operations and Production Management, 14(4): 26-49.
Karsak, E.E., 2002, Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives, International Journal of Production Research, 40(13): 3167-3181.
Karsak, E.E., and Tolga, E., 2001, Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments, International Journal of Production Economics, 69: 49-64.
Lenz, J.E., 1988, Flexible Manufacturing, Benefits For The Low-Inventory Factory, Marcel Dekker, Inc., New York.
Meredith, J.R., and Suresh, N.C., 1986, Justification techniques for advanced manufacturing technologies, International Journal of Production Research, 24: 1043-1057.
Miltenburg, G.J., and Krinsky, I., 1987, Evaluating flexible manufacturing systems, IEEE Transactions, 19: 222-233.
Nagarur, N., 1992, Some performance measures of flexible manufacturing systems, International Journal of Production Research, 30: 799-809.
Nelson, C.A., 1986, A scoring model for flexible manufacturing systems project selection, European Journal of Operational Research, 24: 346-359.
Rai, R., Kameshwaran, S., and Tiwari, M.K., 2002, Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm, International Journal of Production Research, 40(3): 641-665.
Saaty, T.L., 1980, The Analytical Hierarchy Process, McGraw-Hill, New York.
Saaty, T.L., 1990, How to make a decision: the analytic hierarchy process, European Journal of Operational Research, 48(1): 9-26.
Saaty, T.L., 1986, Exploring optimization through hierarchies and ratio scales, Socio- Economic Planning Sciences, 20(6): 355-360.
Sambasivarao, K.V., and Deshmukh, S.G., 1997, A decision support system for selection and justification of advanced manufacturing technologies, Production Planning and Control, 8: 270-284.
Sarkis, J., and Talluri, S., 1999, A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors, International Journal of Production Research, 37(13): 2927-2938.
Shang, J., and Sueyoshi, T., 1995, A unified framework for the selection of a flexible manufacturing system, European Journal of Operational Research, 85: 297-315.
Stam, A., and Kuula, M., 1991, Selecting a flexible manufacturing system using multiple criteria analysis, International Journal of Production Research, 29: 803-820.
Tabucanon, M.T., Batanov, D.N., and Verma, D.K., 1994, Decision support system for multicriteria machine selection for flexible manufacturing systems, Computers in Industry, 25 (2): 131-143.
Trentesaux, D., Dindeleux, R., and Tahon, C., 1998, A multicriteria decision support system for dynamic task allocation in a distributed production activity control structure. International Journal of Computer Integrated Manufacturing, 11(1): 3-17.
Vasant, P., Bhattacharya, A., and Barsoum, N. N., 2005, Fuzzy patterns in multi-level of satisfaction for MCDM model using smooth S-Curve MF, in:, Lecture Notes in Artificial Intelligence, Wang, L. and Jin, Y., (eds.), Springer-Verlag: Berlin, 3614: 1294-1303.
Wabalickis, R.N., 1988, Justification of FMS with the analytic hierarchy process, Journal of Manufacturing Systems, 7: 175-182.
Wang, T.Y., Shaw, C.F., and Chen, Y.-L., 2000, Machine selection in flexible manufacturing cell: a fuzzy multiple attribute decision-making approach. International Journal of Production Research, 38(9): 2079-2097.
Yurdakul, M., 2004, AHP as a strategic decision-making tool to justify machine tool selection, Journal of Materials Processing Technology, 146(3): 365-376.
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Bhattacharya, A., Abraham, A., Vasant, P. (2008). FMS Selection Under Disparate Level-of-Satisfaction of Decision Making Using an Intelligent Fuzzy-MCDM Model. In: Kahraman, C. (eds) Fuzzy Multi-Criteria Decision Making. Springer Optimization and Its Applications, vol 16. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76813-7_10
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DOI: https://doi.org/10.1007/978-0-387-76813-7_10
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