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From MCDA to Fuzzy MCDA: violation of basic axiom and how to fix it

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Abstract

The use of fuzzy numbers (FNs) for managing uncertainty in multi-criteria decision analysis (MCDA) demands a thorough exploring multi-criteria decision problem under fuzzy environment. Fuzzy MCDA (FMCDA) model implies comparison, choice or ranking alternatives based on assessing corresponding functions with subsequent ranking of FNs. Despite the wide use of FMCDA in recent decades, the effect of the violation of axioms for fuzzy ranking methods on FMCDA models has not been explored yet. This paper aims at demonstrating the violation of the basic MCDA axiom, associated with ranking of dominating and dominated in Pareto alternatives, by fuzzy TOPSIS and fuzzy MAVT models as an example. The suggestion to implement FMCDA models in applications without violation of the basic axiom is elicited based on the use of distinguishable fuzzy numbers.

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References

  1. Adamo JM (1980) Fuzzy decision trees. Fuzzy Sets Syst 4(3):207–219

    MathSciNet  MATH  Google Scholar 

  2. Baas SM, Kwakernaak H (1977) Rating and ranking of multiple-aspect alternatives using fuzzy sets. Automatica 13:47–58

    MathSciNet  MATH  Google Scholar 

  3. Behzadian M, Otaghsara SK, Yazdani M, Ignatius J (2012) A state of the art survey of topsis applications. Expert Syst Appl 39:13051–13069

    Google Scholar 

  4. Belton V, Stewart T (2002) Multiple criteria decision analysis: an integrated approach. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  5. Bourbaki N (1998) General topology: chapters 5–10, elements of mathematics. Springer, Berlin

    MATH  Google Scholar 

  6. Brunelli M, Mezei J (2013) How different are ranking methods for fuzzy numbers? A numerical study. Int J Approx Reason 54:627–639

    MathSciNet  MATH  Google Scholar 

  7. Campos LM, González A (1989) A subjective approach for ranking fuzzy numbers. Fuzzy Sets Syst 29(2):145–153

    MathSciNet  MATH  Google Scholar 

  8. Chen C-T (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1):1–9

    MATH  Google Scholar 

  9. Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making: methods and applications, vol 375. Springer, Berlin

    MATH  Google Scholar 

  10. Clemente M, Fernández FR, Puerto J (2011) Pareto-optimal security strategies in matrix games with fuzzy payoffs. Fuzzy Sets Syst 176(1):36–45 (Theme: decision and games)

    MathSciNet  MATH  Google Scholar 

  11. Dubois D, Prade H (1983) Ranking fuzzy numbers in the setting of possibility theory. Inf Sci 30:183–224

    MathSciNet  MATH  Google Scholar 

  12. Dubois D, Prade H (1978) Operations on fuzzy numbers. Int J Syst Sci 9(6):613–626

    MathSciNet  MATH  Google Scholar 

  13. Dyer JS (2005) MAUT–Multiattribute utility theory. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis. State of the art surveys. Springer, Berlin

    Google Scholar 

  14. Dymova L, Sevastjanov P, Tikhonenko A (2013) An approach to generalization of fuzzy TOPSIS method. Expert Syst Appl 238:149–162

    MathSciNet  Google Scholar 

  15. Figueira J, Greco S, Ehrgott M (eds) (2005) Multiple criteria decision analysis. State of the art surveys. Springer, Berlin

    MATH  Google Scholar 

  16. Greco S, Ehrgott M, Figueira JR (eds) (2016) Multiple criteria decision analysis state of the art surveys, vol 233. International series in operations research and management science. Springer, Berlin

    MATH  Google Scholar 

  17. Hanss M (2005) Applied fuzzy arithmetic. Springer, Berlin

    MATH  Google Scholar 

  18. Hwang C-L, Yoon K (1981) Multiple attribute decision making: methods and applications, vol 186. Lecture notes in economics and mathematical systems. Springer, Berlin

    MATH  Google Scholar 

  19. Ishizaka A, Nemery P (2013) Multicriteria decision analysis: methods and software. Wiley, New York

    Google Scholar 

  20. Jae DK, Eunho LM, Eunjin J, Dug HH (2017) Ranking methods for fuzzy numbers: the solution to Brunelli and Mezei’s conjecture. Fuzzy Sets Syst 315:109–113

    MathSciNet  MATH  Google Scholar 

  21. Jain V, Sangaiah AK, Sakhuja S, Thoduka N, Aggarwal R (2018) Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput Appl 29(7, SI):555–564

    Google Scholar 

  22. Kahraman C, Cevik Onar S, Oztaysi B (2015) Fuzzy multicriteria decision-making: a literature review. Int J Comput Intell Syst 8(4):637–666

    MATH  Google Scholar 

  23. Kahraman C, Tolga AC (2009) An alternative ranking approach and its usage in multi-criteria decision-making. Int J Comput Intell Syst 2(3):219–235

    Google Scholar 

  24. Kaya T, Kahraman C (2011) Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Syst Appl 38(6):6577–6585

    Google Scholar 

  25. Keeney RL, Raiffa H (1976) Decisions with multiple objectives: preferences and value tradeoffs. Wiley, New York

    MATH  Google Scholar 

  26. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Simon & Schuster, Upper Saddle River

    MATH  Google Scholar 

  27. Lee KH (2005) First course on fuzzy theory and applications, volume advances in soft computing. Springer, Berlin

    Google Scholar 

  28. Li D-F (2010) A ratio ranking method of triangular intuitionistic fuzzy numbers and its application to MADM problems. Comput Math Appl 60(6):1557–1570

    MathSciNet  MATH  Google Scholar 

  29. Martínez L, Liu J, Yang JB, Herrera F (2005) A multigranular hierarchical linguistic model for design evaluation based on safety and cost analysis. Int J Intell Syst 20(12):1161–1194

    MATH  Google Scholar 

  30. Nakamura K (1986) Preference relations on a set of fuzzy utilities as a basis for decision making. Fuzzy Sets Syst 20(2):147–162

    MathSciNet  MATH  Google Scholar 

  31. Nguyen HT, Kreinovich V, Wu B, Xiang G (2012) Computing statistics under interval and fuzzy uncertainty. Springer, Berlin

    MATH  Google Scholar 

  32. Pedrycz W, Ekel P, Parreiras R (2011) Fuzzy multicriteria decision-making: models, methods and applications. Wiley, Chichester

    MATH  Google Scholar 

  33. Ponnialagan D, Selvaraj J, Velu LGN (2018) A complete ranking of trapezoidal fuzzy numbers and its applications to multi-criteria decision making. Neural Comput Appl 30(11):3303–3315

    Google Scholar 

  34. Rodríguez RM, Labella A, Martínez L (2016) An overview on fuzzy modelling of complex linguistic preferences in decision making. Int J Comput Intell Syst 9(81–94):2016

    Google Scholar 

  35. Roy B (1996) Multicriteria methodology for decision aiding. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  36. Rudin W (1976) Principles of mathematical analysis, 3rd edn. McGraw Hill, New York

    MATH  Google Scholar 

  37. Sangaiah AK, Gopal J, Basu A, Subramaniam PR (2017) An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Comput Appl 28(1):111–123

    Google Scholar 

  38. Stefanini L, Sorini L, Guerra ML (2008) Fuzzy numbers and fuzzy arithmetic. In: Pedrycz W, Skowron A, Kreinovich V (eds) Handbook of granular computing. Wiley, New York

    MATH  Google Scholar 

  39. Tanaka H, Asai K (1984) Fuzzy linear programming problems with fuzzy numbers. Fuzzy Sets Syst 13(1):1–10

    MathSciNet  MATH  Google Scholar 

  40. Wang J-C, Chen T-Y (2018) Multiple criteria decision analysis using correlation-based precedence indices within pythagorean fuzzy uncertain environments. Int J Comput Intell Syst 11:911–924

    Google Scholar 

  41. Wang X, Kerre EE (2001) Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets Syst 118(3):375–385

    MathSciNet  MATH  Google Scholar 

  42. Wang X, Kerre EE (2001) Reasonable properties for the ordering of fuzzy quantities (II). Fuzzy Sets Syst 118(3):387–405

    MathSciNet  MATH  Google Scholar 

  43. Wang X, Ruan D, Kerre EE (2009) Mathematics of fuzziness basic issues. Springer, Berlin

    MATH  Google Scholar 

  44. Yager RR (1980) On choosing between fuzzy subsets. Kybernetes 9(2):151–154

    MATH  Google Scholar 

  45. Yager RR (1981) A procedure for ordering fuzzy subsets of the unit interval. Inf Sci 24(2):143–161

    MathSciNet  MATH  Google Scholar 

  46. Yatsalo B, Korobov A, Martínez L (2017) Fuzzy multi-criteria acceptability analysis: a new approach to multi-criteria decision analysis under fuzzy environment. Expert Syst Appl 84:262–271

    Google Scholar 

  47. Yatsalo B, Korobov A, Oztaysi B, Kahraman C, Martínez L (2020) A general approach to fuzzy TOPSIS based on the concept of fuzzy multicriteria acceptability analysis. J Intell Fuzzy Syst 38:979–995

    Google Scholar 

  48. Yatsalo B, Martínez L (2018) Fuzzy rank acceptability analysis: a confidence measure of ranking fuzzy numbers. IEEE Trans Fuzzy Syst 26:3579–3593

    Google Scholar 

  49. Yuan Y (1991) Criteria for evaluating fuzzy ranking methods. Fuzzy Sets Syst 44:139–157

    MathSciNet  MATH  Google Scholar 

  50. Yue O, Liangzhong Y, Bin Z, Zheng P (2018) The linguistic intuitionistic fuzzy set topsis method for linguistic multi-criteria decision makings. Int J Comput Intell Syst 11:120–132

    Google Scholar 

  51. Zadeh L (1975) The concept of a linguistic variable and its applications to approximate reasoning. Inf Sci Part I, II, III, (8,9):199–249, 301–357, 43–80

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Acknowledgements

This work is supported by the Russian National research project RFBR-19-07-01039 and the Spanish National research project PGC2018-099402-B-I00 and ERDF.

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Correspondence to Luis Martínez.

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Yatsalo, B., Korobov, A. & Martínez, L. From MCDA to Fuzzy MCDA: violation of basic axiom and how to fix it. Neural Comput & Applic 33, 1711–1732 (2021). https://doi.org/10.1007/s00521-020-05053-9

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