Abstract
Assigning alternatives to predefined ordered categories under multicriteria conditions is the essence of multi-criteria sorting problematic. The family of fuzzy multi-criteria sorting models with the common name FTOPSIS-Sort are introduced based on the fuzzy extension of Multi-Criteria Decision Analysis (MCDA) ordinary method TOPSIS with the use of different approaches to assess functions of fuzzy numbers and different fuzzy ranking methods. The features of adjusting Fuzzy TOPSIS (FTOPSIS) models to sorting problematic are presented. The developed FTOPSIS-Sort models are implemented for multi-criteria sorting of non-pharmaceutical interventions against COVID-19.
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References
Alkan, N., Kahraman, C.: Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method. Appl. Soft Comput. 110, 107653 (2021). https://doi.org/10.1016/j.asoc.2021.107653
Alvarez, P.A., Ishizaka, A., Martínez, L.: Multiple-criteria decision-making sorting methods: a survey. Exp. Syst. Appl. 183, 115368 (2021)
Campos, A.C.S.M., Mareschal, B., de Almeida, A.T.: Fuzzy FlowSort: an integration of the FlowSort method and fuzzy set theory for decision making on the basis of inaccurate quantitative data. Inf. Sci. 293, 115–124 (2015)
Chen, C.T.: Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114(1), 1–9 (2000)
Hanss, M.: Applied Fuzzy Arithmetic. Springer, Heidelberg (2005). https://doi.org/10.1007/b138914
Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, vol. 186. Springer, Berlin (1981). https://doi.org/10.1007/978-3-642-48318-9
Kahraman, C., Onar, S.C., Oztaysi, B.: Fuzzy multicriteria decision-making: a literature review. Int. J. Comput. Intell. Syst. 8(4), 637–666 (2015)
Krejčí, J., Ishizaka, A.: FAHPSort: a fuzzy extension of the AHPSort method. Int. J. Inf. Technol. Decis. Making 17(04), 1119–1145 (2018). https://doi.org/10.1142/s0219622018400011
Liu, J., Xu, Z., Qin, J.: A sorting method: BWMSort II in interval type-2 fuzzy environment. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2019)
Olson, D.: Comparison of weights in TOPSIS models. Math. Comput. Modell. 40(7-8), 721–727 (2004)
Pereira, J., de Oliveira, E.C.B., Gomes, L.F.A.M., Araújo, R.M.: Sorting retail locations in a large urban city by using ELECTRE TRI-c and trapezoidal fuzzy numbers. Soft. Comput. 23(12), 4193–4206 (2019)
Remadi, F.D., Frikha, H.M.: The FlowSort for multi criteria decision making in intuitionistic fuzzy environment. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 238–244. IEEE (2019)
Roy, B.: Multicriteria Methodology for Decision Aiding. Springer, New York (1996). https://doi.org/10.1007/978-1-4757-2500-1
Samanlioglu, F., Kaya, B.E.: Evaluation of the COVID-19 pandemic intervention strategies with hesitant f-AHP. J. Healthc. Eng. 2020, 1–11 (2020)
Sayan, M., Yildirim, F.S., Sanlidag, T., Uzun, B., Ozsahin, D.U., Ozsahin, I.: Capacity evaluation of diagnostic tests for COVID-19 using multicriteria decision-making techniques. Comput. Math. Meth. Med. 2020, 1–8 (2020). https://doi.org/10.1155/2020/1560250
Wang, X., Ruan, D., Kerre, E.: Mathematics of Fuzziness Basic Issues (2009). https://doi.org/10.1007/978-3-540-78311-4
Yatsalo, B., Korobov, A., Martínez, L.: From MCDA to Fuzzy MCDA: violation of basic axiom and how to fix it. Neural Comput. Appl. 33(5), 1711–1732 (2021). https://doi.org/10.1007/s00521-020-05053-9
Yatsalo, B., Korobov, A., Oztaysi, B., Kahraman, C., Martínez, L.: A general approach to Fuzzy TOPSIS based on the concept of fuzzy multicriteria acceptability analysis. J. Intell. Fuzzy Syst. 38, 979–995 (2020)
Yatsalo, B., Martínez, L.: Fuzzy rank acceptability analysis: a confidence measure of ranking fuzzy numbers. IEEE Trans. Fuzzy Syst. 26, 3579–3593 (2018)
Yatsalo, B., Radaev, A., Martínez, L.: From MCDA to fuzzy MCDA: presumption of model adequacy or is every fuzzification of an mCDA method justified? Inf. Sci. 587, 371–392 (2022). https://doi.org/10.1016/j.ins.2021.12.051
Zopounidis, C., Doumpos, M.: Multicriteria classification and sorting methods: a literature overview. Eur. J. Oper. Res. 138, 229–246 (2002)
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Radaev, A., Haktanir, E., Yatsalo, B., Kahraman, C. (2022). Classification of Non-pharmaceutical Anti-COVID Interventions Based on Novel FTOPSIS-Sort Models. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-09173-5_9
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