Compromise Ratio Method for Decision Making under Fuzzy Environment using Fuzzy Distance Measure
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32799
Compromise Ratio Method for Decision Making under Fuzzy Environment using Fuzzy Distance Measure

Authors: Debashree Guha, Debjani Chakraborty

Abstract:

The aim of this paper is to adopt a compromise ratio (CR) methodology for fuzzy multi-attribute single-expert decision making proble. In this paper, the rating of each alternative has been described by linguistic terms, which can be expressed as triangular fuzzy numbers. The compromise ratio method for fuzzy multi-attribute single expert decision making has been considered here by taking the ranking index based on the concept that the chosen alternative should be as close as possible to the ideal solution and as far away as possible from the negative-ideal solution simultaneously. From logical point of view, the distance between two triangular fuzzy numbers also is a fuzzy number, not a crisp value. Therefore a fuzzy distance measure, which is itself a fuzzy number, has been used here to calculate the difference between two triangular fuzzy numbers. Now in this paper, with the help of this fuzzy distance measure, it has been shown that the compromise ratio is a fuzzy number and this eases the problem of the decision maker to take the decision. The computation principle and the procedure of the compromise ratio method have been described in detail in this paper. A comparative analysis of the compromise ratio method previously proposed [1] and the newly adopted method have been illustrated with two numerical examples.

Keywords: Compromise ratio method, Fuzzy multi-attributesingle-expert decision making, Fuzzy number, Linguistic variable

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333138

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1362

References:


[1] Deng- Feng Li, "Compromise ratio method for fuzzy multiattribute group decision making", Applied Soft Computing vol. 7, pp. 807-817, 2007.
[2] C. Chakraborty, D. Chakraborty, "A theoretical development on fuzzy distance measure for fuzzy numbers", Mathematical and Computer Modeling, vol. 43, pp. 254-261, 2006.
[3] D. Chakraborty, "Structural quantization of vagueness in linguistic expert opinions in an evaluation programme", Fuzzy Sets Systems. Vol. 119, pp. 171-186, 2001.
[4] C. Chakraborty, D. Chakraborty, "A decision scheme based on OWA operator for an evaluation .programme, an approximate reasoning approach", Applied Soft computing, vol. 5, pp. 45-53, 2004.
[5] D. Li, Fuzzy, "Multiobjective Many-Person Decision making game and Games", National defense Industy Press.Beijing.2003.
[6] Y- M. Wang, J-B. Yang, D-L. Xu, K-S. Chin, "On the centroids of fuzzy numbers", Fuzzy Sets and Systems, vol. 157, pp. 919-926, 2006.
[7] C.L.Hwang. K.Yoon, "Multiple Attributes Decision Making Methods and Applications", Springer, Berlin. Heidelberg. 1981.
[8] S.J.Chen. C.L.Hwang, "Fuzzy Multiple Attribute Decision Making: Methods and Applications", Springer-Verlag Berlin, 1992.
[9] R.-C. Wang, S.-J.Chuu, "Group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system", European Journal of .Operational. Research, vol. 154 , pp. 563-572, 2004 .
[10] R.E.Bellman, L.A.Zadeh, "Decision making in a fuzzy Environment", Management Science, vol. 17, pp. 141- 164, 1970.
[11] M.Delgado, J.L.Verdegay, M.A.Vila, "Linguistic decision-making models", Int. J. Intelligent System, vol. 7, pp. 479-492,1992.
[12] F.Herrera, E.Herrera-Viedma, J.L.Verdagay, "A model of Consensus in group decision making under linguistic assessments", Fuzzy Sets and Systems, vol. 78, pp. 73- 87, 1996.
[13] L.A.Zadeh, "The concept of a linguistic variables and itsapplication to approximate reasoning", Inform-Science, vol. 8, pp. 199-249(I), pp. 301- 357 (II), 1975.
[14] C.T. Chen, "Extension of the TOPSIS for group decision making under fuzzy environment", Fuzzy Sets and systems, vol. 114, pp. 1-9, 2000.
[15] S.M. Chen, "A new approach to handling fuzzy decision- making problems", IEEE Trans. Systems Man Cybernatics, vol. 18, pp. 1012- 1016, 1988.
[16] D. Chakraborty, "Estimation of aggregative risk in software development: an approximate reasoning approach, in: Proceedings of the Conference Fuzzy Set Theory and its Mathematical Aspects and Applications, BHU, Varanasi, December 26-28, 2002, Fuzzy Set theory Math .Aspects Application, Allied Publishers, pp. 109-116.
[17] C.Chakraborty, D.Chakraborty, "Approximate reasoning with OWA operator in an evaluation scheme", Combinatorial and Computational Mathematics, Narosa Pub, New Delhi, 2004, pp. 123- 132.
[18] R. Biswas, "An application of fuzzy sets in students- Evaluation", Fuzzy Sets Systems, vol. 74, pp. 187-194, 1995.
[19] D. Zhou, J. Ma, E. Turban, N. Bolloju, "A fuzzy set approach to the evaluation of journal grades", Fuzzy Sets Syst. Vol. 131, pp. 63-74, 2002.