Abstract
Decision making is a line of research that has been consolidating since its beginnings in the 1960s. The aim of this paper is to show the evolution and future challenges of this line of research, focusing especially on its evaluation and information methods. For this purpose, some issues and trends of the fuzzy decision system are presented. By doing so, it is possible to show which trend fuzzy decision systems will follow and the challenges that may arise.
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References
Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)
Atanassov, K.T., Pasi, G., Yager, R.R.: Intuitionistic fuzzy interpretations of multi-person multi-criteria decision making. In: Proceedings First International IEEE Symposium Intelligent Systems, Varna, Bulgaria, pp. 115–119, September 2002
Bellman, R.E., Zadeh, L.A.: Decision making in a fuzzy environment. Manag. Sci. 17, 141–164 (1970)
Bilgiç, T.: Interval-valued preference structures. Eur. J. Oper. Res. 105(1), 162–183 (1998)
Brans, J.P., Vincke, P., Mareschal, B.: How to select and how to rank projects: the PROMETHEE method. Eur. J. Oper. Res. 24(2), 228–238 (1986)
Cabrerizo, F.J., Ureña, R., Pedrycz, W., Herrera-Viedma, E.: Building consensus in group decision making with an allocation of information granularity. Fuzzy Sets Syst. 255, 115–127 (2014)
Cabrerizo, F.J., Trillo, J.R., Morente-Molinera, J.A., Alonso, S., Herrera-Viedma, E.: A granular consensus model based on intuitionistic reciprocal preference relations and minimum adjustment for multi-criteria group decision making. In: 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP), pp. 298–305. Atlantis Press (2021)
del Moral, M.J., Chiclana, F., Tapia, J.M., Herrera-Viedma, E.: A comparative study on consensus measures in group decision making. Int. J. Intell. Syst. 33(8), 1624–1638 (2018)
Deschrijver, G., Kerre, E.E.: On the relationship between some extensions of fuzzy set theory. Fuzzy Sets Syst. 133(2), 227–235 (2003)
Dijkman, J.G., van Haeringen, H., de Lange, S.J.: Fuzzy numbers. J. Math. Anal. Appl. 92(2), 301–341 (1983)
Gao, J., Guo, F., Ma, Z., Huang, X., Li, X.: Multi-criteria group decision making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators. Energy 204, 117899 (2020)
Govindan, K., Jepsen, M.B.: ELECTRE: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 250(1), 1–29 (2016)
Greco, S., Ehrgott, M., Figueira, J.R. (eds.): Multiple Criteria Decision Analysis: State of the Art Surveys. Springer-Verlag, New York (2016)
Hafezalkotob, A., Liao, H., Herrera, F.: An overview of MULTIMOORA for multi-criteria decision making: theory, developments, applications, and challenges. Inf. Fusion 51, 145–177 (2019)
Herrera, F., Alonso, S., Chiclana, F., Herrera-Viedma, E.: Computing with words in decision making: foundations, trends and prospects. Fuzzy Optim. Decis. Making 8(4), 337–364 (2009). https://doi.org/10.1007/s10700-009-9065-2
Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: Direct approach processes in group decision making using linguistic OWA operators. Fuzzy Sets Syst. 79(2), 175–190 (1996)
Hwang, C.-L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey. Springer-Verlag, Berlin, Heidelberg (1981)
Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets Syst. 18(2), 105–118 (1986)
Morente-Molinera, J.A., Cabrerizo, F.J., Trillo, J.R., Pérez, I.J., Herrera-Viedma, E.: Managing group decision making criteria values using fuzzy ontologies. Procedia Comput. Sci. 199, 166–173 (2022)
Moro, L.M., Ramos, A.: Goal programming approach to maintenance scheduling of generating units in large scale power systems. IEEE Trans. Power Syst. 14(3), 1021–1028 (1999)
Opricovic, S., Tzeng, G.-H.: Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2), 445–455 (2004)
Orlovsky, S.A.: Decision-making with a fuzzy preference relation. Fuzzy Sets Syst. 1(3), 155–167 (1978)
Palomares, I., Estrella, F.J., Martínez, L., Herrera, F.: Consensus under a fuzzy context: taxonomy, analysis framework AFRYCA and experimental case of study. Inf. Fusion 20, 252–271 (2014)
Pérez, I.J., García-Sánchez, P., Cabrerizo, F.J., Herrera-Viedma, E.: An approach toward a feedback mechanism for consensus reaching processes using gamification to increase the experts’ experience. In: Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS 53), Maui, Hawaii, USA, pp. 1717–1726, January 2020
Rodríguez, R.M., et al.: A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress. Inf. Fusion 29, 89–97 (2016)
Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2012)
Roy, B.: Paradigms and challenges. In: Greco, S., Ehrgott, M., Figueira, J.R. (eds.) Multiple Criteria Decision Analysis. ISORMS, vol. 233, pp. 19–39. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3094-4_2
Cristóbal, J.R.S.: Multi-criteria decision-making in the selection of a renewable energy project in Spain: the Vikor method. Renewable Energy 36(2), 498–502 (2011)
Sekitani, K., Takahashi, I.: A unified model and analysis for AHP and ANP. J. Oper. Res. Soc. Jpn. 44(1), 67–89 (2001)
Shendrik, M.G., Tamm, G.B.: An approach to interactive solution of multicriterial optimization problems with linquistic modeling of preferences. Autom. Control. Comput. Sci. 19(6), 1–7 (1986)
Shih, H.-S., Shyur, H.-J., Stanley Lee, E.: An extension of Topsis for group decision making. Math. Comput. Model. 45(7–8), 801–813 (2007)
Szmidt, E., Kacprzyk, J.: A consensus-reaching process under intuitionistic fuzzy preference relations. Int. J. Intell. Syst. 18(7), 837–852 (2003)
Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)
Trillo, J.R., Herrera-Viedma, E., Cabrerizo, F.J., Morente-Molinera, J.A.: A multi-criteria group decision making procedure based on a multi-granular linguistic approach for changeable scenarios. In: Fujita, H., Selamat, A., Lin, J.C.-W., Ali, M. (eds.) IEA/AIE 2021. LNCS (LNAI), vol. 12799, pp. 284–295. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79463-7_24
van Laarhoven, P.J.M., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11(1–3), 229–241 (1983)
Wang, H., Xu, Z.S., Zeng, X.-J.: Modeling complex linguistic expressions in qualitative decision making: an overview. Knowl.-Based Syst. 144, 174–187 (2018)
Wen, T.-C., Lai, H.-H., Chang, K.-H.: A new flexible method for solving multi-expert multi-criterion decision-making problems. Appl. Sci. 10(13), 4582 (2020)
Wu, Y., et al.: Distributed linguistic representations in decision making: taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Inf. Fusion 65, 165–178 (2021)
Zeshui, X., Zhao, N.: Information fusion for intuitionistic fuzzy decision making: an overview. Inf. Fusion 28, 10–23 (2016)
Yager, R.R.: Non-numeric multi-criteria multi-person decision making. Group Decis. Negot. 2, 81–93 (1993). https://doi.org/10.1007/BF01384404
Zadeh, L.A.: Fuzzy sets. Inf. Control 5(3), 338–353 (1965)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning–II. Inf. Sci. 8(4), 301–357 (1975)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning–III. Inf. Sci. 9(1), 43–80 (1975)
Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)
Zhang, G.Q., Dong, Y.C., Xu, Y.F.: Consistency and consensus measures for linguistic preference relations based on distribution assessments. Inf. Fusion 17, 46–55 (2014)
Zhang, H., Zhao, S., Kou, G., Li, C.-C., Dong, Y.C., Herrera, F.: An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Inf. Fusion 60, 65–79 (2020)
Zuheros, C., Li, C.-C., Cabrerizo, F.J., Dong, Y.C., Herrera-Viedma, E., Herrera, F.: Computing with words: revisiting the qualitative scale. Internat. J. Uncertain. Fuzziness Knowl.-Based Syst. 26(Suppl. 2), 127–143 (2018)
Acknowledgement
This work was supported by the project B-TIC-590-UGR20 co-funded by the Programa Operativo FEDER 2014–2020 and the Regional Ministry of Economy, Knowledge, Enterprise and Universities (CECEU) of Andalusia, by the Andalusian Government through the project P20_00673, and by the project PID2019-103880RB-I00 funded by MCIN/AEI/10.13039/501100011033.
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Trillo, J.R., Cabrerizo, F.J., Chiclana, F., Martínez, M.A., Herrera-Viedma, E. (2023). Some Trends in Fuzzy Decision Making. In: Dzitac, S., Dzitac, D., Filip, F.G., Kacprzyk, J., Manolescu, MJ., Oros, H. (eds) Intelligent Methods Systems and Applications in Computing, Communications and Control. ICCCC 2022. Advances in Intelligent Systems and Computing, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-031-16684-6_28
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