SIMILARITY BASED FUZZY INFERIOR RATIO FOR SOLVING MULTICRITERIA DECISION MAKING PROBLEMS
DOI:
https://doi.org/10.24191/mjoc.v5i2.8721Keywords:
Compromise solution, Fuzzy Inferior Ratio, Fuzzy decision making, Similarity measureAbstract
The method of Fuzzy Inferior Ratio (FIR) has been recognized as one of advantageous methods in multi criteria decision-making under fuzzy environment as it considers the element of compromise solution between the positive and negative aspect of the evaluation simultaneously. It is considered as an improvised version of Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for solving decision-making problems. However, the FIR utilizes the distance approach in the evaluation of obtaining the compromise solution. A defuzzification process is carried out to transform the fuzzy values into a crisp form. Hence, loss of information may occur in the computation. In this paper, we proposed a similarity-based FIR that overcomes the above-mentioned problem. A new compromise solution for the proposed FIR is developed and an improvised procedure of FIR is suggested using the similarity measure approach. A comparative analysis between the distance based and the similarity-based FIR is carried out using a case study of preferred client selection for a loan application. The proposed method is found to be effective in solving decision-making problems as the utilization of similarity measure will sufficiently preserve the data information in the computational process of evaluation.
References
Ahmad, S. A. S., Mohamad, D., Sulaiman, N. H., Shariff, J. M., & Abdullah, K. (2018). A distance and set theoretic-based similarity measure for generalized trapezoidal fuzzy numbers. In AIP Conference Proceedings (Vol. 1974, No. 1, p. 020043). AIP Publishing LLC.
Bellman, R., & Zadeh, L. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17(4), B141-B164.
Bozbura, F. T., Beskese, A., & Kahraman, C. (2007). Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems with Applications, 32(4), 1100-1112.
Chen, C. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
Chen, S. J., & Chen, S. M. (2003). Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Transactions on fuzzy systems, 11(1), 45-56.
Chen, S. M. (1996). New methods for subjective mental workload assessment and fuzzy risk analysis. Cybernetics & Systems, 27(5), 449-472.
Hadi-Vencheh, A., & Mirjaberi, M. (2014). Fuzzy inferior ratio method for multiple attribute decision making problems. Information Sciences, 277, 263-272.
Hejazi, S., Doostparast, A., & Hosseini, S. (2011). An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers. Expert Systems with Applications, 38(8), 9179-9185.
Janic, M., & Reggiani, A. (2002). An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. European Journal of Transport and Infrastructure Research, 2(2).
Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609.
Lee, H. S. (1999). An optimal aggregation method for fuzzy opinions of group decision. In Systems, Man, and Cybernetics, 1999. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on (Vol. 3, pp. 314-319). IEEE.
Mohamad, D., Ramlan, N. S. A., & Ahmad, S. A. S. (2019). An improvised similarity measure for generalized fuzzy numbers. Bulletin of Electrical Engineering and Informatics, 8(4), 1232-1238.
Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146-156.
Niyigena, L., Luukka, P., & Collan, M. (2012). Supplier evaluation with fuzzy similarity based fuzzy TOPSIS with new fuzzy similarity measure. 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI).
Nourianfar, K., & Montazer, G. A. (2013, May). A fuzzy MCDM approach based on COPRAS method to solve supplier selection problems. In The 5th Conference on Information and Knowledge Technology (pp. 231-235). IEEE.
Obayiuwana, E., & Falowo, O. (2015). A MULTIMOORA approach to access network selection process in heterogeneous wireless networks. In AFRICON 2015 (pp. 1-5). IEEE.
Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications, 38(10), 12983-12990.
Ramani, T. L., Quadrifoglio, L., & Zietsman, J. (2010). Accounting for nonlinearity in the MCDM approach for a transportation planning application. IEEE Transactions on Engineering Management, 57(4), 702-710.
Wei, S. H., & Chen, S. M. (2009). A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Expert Systems with Applications, 36(2009), 589-598.
Wu, P., Zhou, L., Chen, H., & Zhou, H. (2020). An improved fuzzy risk analysis by using a new similarity measure with center of gravity and area of trapezoidal fuzzy numbers. Soft Computing, 24(6), 3923-3936.
Yong, D., Wenkang, S., Feng, D., & Qi, L. (2004). A new similarity measure of generalized fuzzy numbers and its application to pattern recognition. Pattern Recognition Letters, 25(8), 875-883.
Zadeh. L.A. (1965). Fuzzy sets. Journal of Information and Control, 8, 338-353.
Zuo, X., Wang, L., & Yue, Y. (2013). A New Similarity Measure of Generalized Trapezoidal Fuzzy Numbers and Its Application on Rotor Fault Diagnosis. Mathematical Problems in Engineering, 2013, 1-10.
Published
Issue
Section
License
Copyright (c) 2020 Sharifah Aniza Sayed Ahmad, Daud Mohamad, Nurul Iffah Azman
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
