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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

  1. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    Article  MATH  Google Scholar 

  2. 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

    Google Scholar 

  3. Bellman, R.E., Zadeh, L.A.: Decision making in a fuzzy environment. Manag. Sci. 17, 141–164 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bilgiç, T.: Interval-valued preference structures. Eur. J. Oper. Res. 105(1), 162–183 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  5. 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)

    Article  MathSciNet  MATH  Google Scholar 

  6. 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)

    Article  MathSciNet  MATH  Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Deschrijver, G., Kerre, E.E.: On the relationship between some extensions of fuzzy set theory. Fuzzy Sets Syst. 133(2), 227–235 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Dijkman, J.G., van Haeringen, H., de Lange, S.J.: Fuzzy numbers. J. Math. Anal. Appl. 92(2), 301–341 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Govindan, K., Jepsen, M.B.: ELECTRE: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 250(1), 1–29 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  13. Greco, S., Ehrgott, M., Figueira, J.R. (eds.): Multiple Criteria Decision Analysis: State of the Art Surveys. Springer-Verlag, New York (2016)

    Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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

    Article  MATH  Google Scholar 

  16. 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)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hwang, C.-L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey. Springer-Verlag, Berlin, Heidelberg (1981)

    Book  MATH  Google Scholar 

  18. Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets Syst. 18(2), 105–118 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  MATH  Google Scholar 

  22. Orlovsky, S.A.: Decision-making with a fuzzy preference relation. Fuzzy Sets Syst. 1(3), 155–167 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. 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

    Google Scholar 

  25. 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)

    Article  Google Scholar 

  26. Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2012)

    Article  Google Scholar 

  27. 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

    Chapter  Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. Sekitani, K., Takahashi, I.: A unified model and analysis for AHP and ANP. J. Oper. Res. Soc. Jpn. 44(1), 67–89 (2001)

    MathSciNet  MATH  Google Scholar 

  30. 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)

    MATH  Google Scholar 

  31. 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)

    Article  MATH  Google Scholar 

  32. Szmidt, E., Kacprzyk, J.: A consensus-reaching process under intuitionistic fuzzy preference relations. Int. J. Intell. Syst. 18(7), 837–852 (2003)

    Article  MATH  Google Scholar 

  33. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)

    MATH  Google Scholar 

  34. 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

    Chapter  Google Scholar 

  35. van Laarhoven, P.J.M., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11(1–3), 229–241 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. 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)

    Article  Google Scholar 

  39. Zeshui, X., Zhao, N.: Information fusion for intuitionistic fuzzy decision making: an overview. Inf. Fusion 28, 10–23 (2016)

    Article  Google Scholar 

  40. Yager, R.R.: Non-numeric multi-criteria multi-person decision making. Group Decis. Negot. 2, 81–93 (1993). https://doi.org/10.1007/BF01384404

    Article  Google Scholar 

  41. Zadeh, L.A.: Fuzzy sets. Inf. Control 5(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  42. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  43. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning–II. Inf. Sci. 8(4), 301–357 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  44. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning–III. Inf. Sci. 9(1), 43–80 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  45. Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)

    Article  Google Scholar 

  46. 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)

    Article  Google Scholar 

  47. 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)

    Article  Google Scholar 

  48. 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)

    Article  MathSciNet  Google Scholar 

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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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