Abstract
Fuzzy TOPSIS has been widely used in solving multi criteria decision making problems. The performance of Fuzzy TOPSIS is dependent on its ability to rank alternatives thus enables decision makers to reach more accurate decisions. Three main steps in the algorithm of Fuzzy TOPSIS include criteria weights determination, calculation of similarity between alternatives and also the closeness coefficient measurements for alternatives in terms of negative and positive ideals. Enhancement at any of these three major steps would lead to a more accurate ranking of alternatives. This paper highlights the ranking ability of five (5) variants of Fuzzy TOPSIS. This is done by applying five different techniques in calculating similarities and/or differences between alternatives. In all the five Fuzzy TOPSIS variants constructed, the data and criteria weights used are similar to the data and criteria weights used in the original Fuzzy TOPSIS proposed by Chen (2000). Thus, the difference in ranking is solely due to the differences when different similarity measures were utilized. The final ranking by the five variants of Fuzzy TOPSIS are observed and analyzed. The finding shows that ranking performance of four Fuzzy TOPSIS variants are consistent to the findings reported in other research. The difference in ranking could be due to the differences of similarity formulation since each similarity measure technique is developed based on different concept thus its ability to find the similarity between any two fuzzy numbers are determined by different criteria involved and this is reflected in the ranking of alternatives. Hence, it can be said that similarity measure calculations have significant impact in the ranking process via Fuzzy TOPSIS.
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References
Chen, C.T.: Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114(1), 1–9 (2000)
Chen, J., Chen, S.: A new method to measure the similarity between fuzzy numbers, pp. 1123–1126 (2001)
Hejazi, S.R., Doostparast, A., Hosseini, S.M.: An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers. Expert Syst. Appl. 38(8), 9179–9185 (2011). https://doi.org/10.1016/j.eswa.2011.01.101
Hsieh, C.H., Chen, S.H.: Similarity of generalized fuzzy numbers with graded mean integration representation. In: Proceedings of 8th International Fuzzy Systems Association World Conference, vol. 2, pp. 551–555 (1999)
Luukka, P.: Fuzzy similarity in multicriteria decision-making problem applied to supplier evaluation and selection in supply chain management. Adv. Artif. Intell. 2011, 1–9 (2011). https://doi.org/10.1155/2011/353509. Article ID 353509
Collan, M., Lukka, P.: Evaluating R&D projects as investments by using an overall ranking from four fuzzy similarity measure-based TOPSIS variants. IEEE Trans. Fuzzy Syst. 22(3), 505–515 (2014)
Onu, P.U., Quan, X., Xu, L., Orji, J., Onu, E.: Evaluation of sustainable acid rain control options utilizing a fuzzy TOPSIS multi-criteria decision analysis model frame work. J. Clean. Prod. 141, 612–625 (2017). https://doi.org/10.1016/j.jclepro.2016.09.065
Peng, C., Du, H., Liao, T.W.: A research on the cutting database system based on machining features and TOPSIS. Robot. Comput. Integr. Manuf. 43, 96–104 (2017). https://doi.org/10.1016/j.rcim.2015.10.011
Walczak, D., Rutkowska, A.: Project rankings for participatory budget based on the fuzzy TOPSIS, 1–9 (2017). http://doi.org/10.1016/j.ejor.2016.12.044
Wei, S.-H., Chen, S.-M.: A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Expert Syst. Appl. 36(1), 589–598 (2009). https://doi.org/10.1016/j.eswa.2007.09.033
Zhang, L., Xu, X., Tao, L.: Some similarity measures for triangular fuzzy number and their applications in multiple criteria group decision-making. J. Appl. Math. 2013, 1–7 (2013)
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Selemin, I.A., Dom, R.M., Shahidin, A.M. (2017). Ranking Performance of Modified Fuzzy TOPSIS Variants Based on Different Similarity Measures. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_21
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DOI: https://doi.org/10.1007/978-981-10-7242-0_21
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