Measurement of Level-of-Satisfaction of Decision Maker in Intelligent Fuzzy-MCDM Theory: A Generalized Approach
The earliest definitions of decision support systems (DSS) identify DSS as systems to support managerial decision makers in unstructured or semiunstructured decision situations. They are also defined as a computer-based information systems used to support decision-making activities in situations where it is not possible or not desirable to have an automated system perform the entire decision process. This chapter aims to delineate measurement of level-of-satisfaction during decision making under an intelligent fuzzy environment. Before proceeding with the multi-criteria decision making model (MCDM), authors try to build a co-relation among DSS, decision theories, and fuzziness of information. The co-relation shows the necessity of incorporating decision makers’ level-of-satisfaction in MCDM models. Later, the authors introduce an MCDM model incorporating different cost factor components and the said level-of-satisfaction parameter. In a later chapter, the authors elucidate an application as well as validation of the devised model. The strength of the proposed MCDM methodology lies in combining both cardinal and ordinal information to get eclectic results from a complex, multi-person and multi-period problem hierarchically.
Key wordsDecision support system level-of-satisfaction in MCDM
Unable to display preview. Download preview PDF.
- Agrell, P., 1995, Interactive multi-criteria decision-making in production economics, profil, series no 15, (Production-Economic Research in Linköping: Linköping, Sweden).Google Scholar
- Arbel, A., and Vargas, L.G., 1990, The analytic hierarchy process with interval judgements, Proceedings of the 9th International Conference of MCDM, Farfaix, VA.Google Scholar
- Bells, S., 1999, Flexible Membership Functions. Available: http://www.louderthanabomb. com/spark_features.html. (Visited on 10 October, 2000).
- Bhattacharya, A., Sarkar, B., and Mukherjee, S.K., 2004, A new method for plant location selection: a holistic approach, International Journal of Industrial Engineering - Theory, Applications and Practice, 11(4): 330-338.Google Scholar
- Boucher, T.O., and Gogus, O., 2002, Reliability, validity and imprecision in fuzzy multi-criteria decision-making, IEEE Transactions on Systems, Man, and Cybernatics - Part C: Applications and Reviews, 32(3): 1-15.Google Scholar
- Davis, G.B., 1974, Management Information Systems, 33, McGraw-Hill, Tokyo.Google Scholar
- Ginzberg, M.J., and Stohr, E.A., 1981, Decision support systems: Issues and perspectives in Proceedings of NYU Symposium on Decision Support Systems, New York.Google Scholar
- Gorry, G.A., and Scott Morton, M.S., 1971, A framework for management information systems, Sloan Management Review, 13(1): 55-70.Google Scholar
- Harris, R., 1998, Introduction to Decision Making. Available: http://www.vanguard.edu/ rharris/crebook5.htm. (Accessed 14 October, 2000).
- Lai, Y.J., and Hwang, C.L., 1994, Fuzzy Multi-Objective Decision Making: Methods and Applications, Spinger-Verlag, Berlin.Google Scholar
- Saaty, T.L., 1990, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York.Google Scholar
- Saaty, T.L., 1980, The Analytical Hierarchy Process, McGraw-Hill, New York.Google Scholar
- Tabucanon, M.T., 1996, Multi objective programming for industrial engineers. In Mathematical Programming for Industrial Engineers, Marcel Dekker, Inc., New York, pp. 487-542.Google Scholar
- Turban, E., 1990, Decision Support and Expert Systems: Management Support Systems, Macmillan, New York.Google Scholar
- Vasant, P., Nagarajan, R., and Yaacob, S., 2002, Decision making using modified S-curve membership function in fuzzy linear programming problem, Journal of Information and Communication Technology, 2: 1-16.Google Scholar
- Wells, H. G., 1908, First and Last Things.Google Scholar
- Zimmermann, H.J., 1987, Fuzzy Sets, Decision Making and Expert Systems, Kluwer Academic Publishers, Boston.Google Scholar