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A new compatibility model for fuzzy group decision making by using trapezoidal fuzzy preference relations with COWA operator

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Abstract

Considering the conflicting opinions and different risk attitudes among decision-makers (DMs) in group decision making (GDM), this paper develops a novel compatibility model with additive trapezoidal fuzzy environment based on continuous ordered weighted averaging (COWA) operator to handle the conflicts. First, some concepts of COWA operator-based compatibility index and characteristic preference relation for additive trapezoidal fuzzy preference relation (ATFPR) are discussed. Then a compatibility reaching algorithm is designed to assist each ATFPR in achieving acceptable compatibility. Moreover, the expert weight optimization model based on the criterion of minimum compatibility of preference relation in GDM is established. Furthermore, a GDM process based on compatibility measures with ATFPRs is introduced, and an application of the proposed approach is put forward. The novelties of our approach are that: (1) COWA operator can deal with the compatibility of all arguments by using controlled parameters that consider the risk attitudes of DMs rather than the compatibility of the simply two points in intervals; (2) compatibility improving algorithm makes sure that the original opinions are retained as much as possible because only one pair of preference relation elements are revised in each round; (3) optimal weights model ensures that weights of DMs in group aggregation are determined availably.

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References

  1. Ban A, Coroianu L, Grzegorzewski P (2011) Trapezoidal approximation and aggregation. Fuzzy Sets Syst 177:45–59

    MathSciNet  Google Scholar 

  2. Büyüközkan G, Mukul E, Kongar E (2021) Health tourism strategy selection via SWOT analysis and integrated hesitant fuzzy linguistic AHP-MABAC approach. Socio-Econ Plan Sci 74:100929

    Google Scholar 

  3. Carra M, Botticini F, Pavesi FC, Maternini G, Pezzagno M, Barabino B (2023) A comparative cycling path selection for sustainable tourism in Franciacorta. An integrated AHP-ELECTRE method. Transport Res Proced 69:448–455

    Google Scholar 

  4. Chen HY, Zhao JB (2004) Research on compatibility of fuzzy judgment matrices. Oper Res Manage Sci 3:44–47

    Google Scholar 

  5. Chen SJ, Chen SM (2007) Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Appl Intell 26:1–11

    Google Scholar 

  6. Dong YC, Zhang H, Herrera-Viedma E (2016) Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decis Support Syst 84:1–15

    Google Scholar 

  7. Gong ZW, Guo WW, Herrera-Viedma E, Gong ZJ, Wei G (2019) Consistency and consensus modeling of linear uncertain preference relations. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2019.10.035

    Article  Google Scholar 

  8. Gong ZW, Lin Y, Yao TX (2013) Uncertain fuzzy preference relations and their applications. Springer, Berlin

    Google Scholar 

  9. Gong ZW, Xu XX (2016) An optimization model of the acceptable consensus and its economic significance. Kybernetes 45:181–206

    MathSciNet  Google Scholar 

  10. Gong Z, Xu X, Guo W, Herrera-Viedma E, Cabrerizo FJ (2020) Minimum cost consensus modelling under various linear uncertain-constrained scenarios. Inf Fusion. https://doi.org/10.1016/j.inffus.2020.08.015

    Article  Google Scholar 

  11. Gou XJ, Xu ZS, Liao HC (2017) Group decision making with compatibility measures of hesitant fuzzy linguistic preference relations. Soft Comput Fusion Found Methodol Appl. https://doi.org/10.1007/s00500-017-2871-5

    Article  Google Scholar 

  12. Heilpen S (1992) The expected value of a fuzzy number. Fuzzy Sets Syst 47:81–86

    MathSciNet  Google Scholar 

  13. Kesberg R, Keller J (2020) Donating to the ‘right’ cause: compatibility of personal values and mission statements of philanthropic organizations fosters prosocial behavior. Personal Indiv Differ. https://doi.org/10.1016/j.paid.2020.110313

    Article  Google Scholar 

  14. Lee LW (2012) Group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency. Expert Syst Appl 39:11666–11676

    Google Scholar 

  15. Liao HC, Mi XM, Xu ZS (2020) A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions. Fuzzy Optim Decis Mak 19:81–134

    MathSciNet  Google Scholar 

  16. Liao HC, Peng XY, Gou XJ (2020) Medical supplier selection with a group decision-making method based on incomplete probabilistic linguistic preference relations. Int J Fuzzy Syst. https://doi.org/10.1007/s40815-020-00885-y

    Article  Google Scholar 

  17. Liao HC, Xu ZS, Zeng XJ, Xu DL (2016) An enhanced consensus reaching process in group decision making with intuitionistic fuzzy preference relations. Inf Sci 329:274–286

    ADS  Google Scholar 

  18. Lin MW, Zhan QS, Xu ZS (2020) Decision making with probabilistic hesitant fuzzy information based on multiplicative consistency. Int J Intell Syst. https://doi.org/10.1002/int.22240

    Article  Google Scholar 

  19. Lin MW, Zhan QS, Xu ZS, Chen RQ (2018) Group decision making with probabilistic hesitant multiplicative preference relations based on consistency and consensus. IEEE Access 6:63329–63344

    Google Scholar 

  20. Liu F, Zhang WG, Zhang LH (2014) Consistency analysis of triangular fuzzy reciprocal preference relations. Eur J Oper Res 235:718–726

    MathSciNet  Google Scholar 

  21. Liu W Q, Zhang H J, Chen X, Yu S (2018) Managing consensus and self-confidence in multiplicative preference relations in group decision making. Knowl-Based Syst 162(15):62–73

    Google Scholar 

  22. Meng FY, Tang J, Xu ZS (2019) Deriving priority weights from intuitionistic fuzzy multiplicative preference relations. Int J Intell Syst. https://doi.org/10.1002/int.22179

    Article  Google Scholar 

  23. Orlovski SA (1978) Decision-making with a fuzzy preference relation. Fuzzy Sets Syst 1:155–167

    MathSciNet  Google Scholar 

  24. Pahade JK, Jha M (2021) Credibilistic variance and skewness of trapezoidal fuzzy variable and meanvariance skewness model for portfolio selection. Results Appl Math 11:100159

    MathSciNet  Google Scholar 

  25. Priyan S, Udayakumar R, Mala P, Prabha M, Ghosh A (2022) A sustainable dual-channel inventory model with trapezoidal fuzzy demand and energy consumption. Clean Eng Technol 20:100400

    Google Scholar 

  26. Rand W (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66:846–850

    Google Scholar 

  27. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  28. Ureña R, Chiclana F, Fujita H, Herrera-Viedma E (2015) Confidence-consistency driven group decision making approach with incomplete reciprocal intuitionistic preference relations. Knowl Based Syst 89:86–96

    Google Scholar 

  29. Wang H, Xu ZS (2015) Some consistency measures of extended hesitant fuzzy linguistic preference relations. Inf Sci 297:316–331

    MathSciNet  Google Scholar 

  30. Wang YL, Chen HY, Zhou LG (2013) Logarithm compatibility of interval multiplicative preference relations with an application to determining optimal weights of experts in the group decision making. Group Decis Negot 22:759–772

    Google Scholar 

  31. Wu P, Zhou LG, Chen HY, Zhou H (2020) An improved fuzzy risk analysis by using a new similarity measure with center of gravity and area of trapezoidal fuzzy numbers. Soft Comput 24:3923–3936

    Google Scholar 

  32. Wu P, Zhu JM, Zhou LG, Chen HY (2019) Automatic iterative algorithm with local revised strategies to improve the consistency of hesitant fuzzy linguistic preference relations. Int J Fuzzy Syst 21:2283–2298

    MathSciNet  Google Scholar 

  33. Wu P, Zhu JM, Zhou LG, Chen HY (2019) Local feedback mechanism based on consistency-derived for consensus building in group decision making with hesitant fuzzy linguistic preference relations. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106001

    Article  Google Scholar 

  34. Xia MM, Chen J (2015) Consistency and consensus improving methods for pairwise comparison matrices based on Abelian linearly ordered group. Fuzzy Sets Syst 266:1–32

    MathSciNet  Google Scholar 

  35. Xu GL, Wan SP, Wang F, Dong JY, Zeng YF (2016) Mathematical programming methods for consistency and consensus in group decision making with intuitionistic fuzzy preference relations. Knowl Based Syst 98:30–43

    Google Scholar 

  36. Xu YJ, Da QL, Liu LH (2009) Normalizing rank aggregation method for priority of a fuzzy preference relation and its effectiveness. Int J Approx Reason 50:1287–1297

    MathSciNet  Google Scholar 

  37. Xu ZS (2001) A practical method for priority of interval number complementary judgment matrix. Oper Res Manage Sci 10:16–19

    Google Scholar 

  38. Xu ZS (2013) Compatibility analysis of Intuitionistic fuzzy preference relations in group decision making. Group Decis Negot 22:463–482

    Google Scholar 

  39. Xu ZS (2004) On compatibility of interval fuzzy preference relations. Fuzzy Optim Decis Mak 3:217–225

    MathSciNet  Google Scholar 

  40. Xu ZS, Da QL (2001) Research on method for ranking interval numbers. Syst Eng 6:94–96

    Google Scholar 

  41. Yager RR (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern B 18:183–190

    Google Scholar 

  42. Yager RR (2004) OWA aggregation over a continuous interval argument with applications to decision making. IEEE Trans Syst Man Cybern Part B 34:1952–1963

    Google Scholar 

  43. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Google Scholar 

  44. Zhai YL, Xu ZS, Liao HC (2019) The stably multiplicative consistency of fuzzy preference relation and interval-valued hesitant fuzzy preference relation. IEEE Access 7:54929–54945

    Google Scholar 

  45. Zhang C, Liao HC, Luo L (2019) Additive consistency-based priority-generating method of q-rung orthopair fuzzy preference relation. Int J Intell Syst 34:2151

    Google Scholar 

  46. Zhang HJ, Zhao SH, Kou G, Li CC, Dong YC, Herrer F (2020) An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Inf Fusion. https://doi.org/10.1016/j.inffus.2020.03.001

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zhang HM (2015) A consistency model for group decision making problems with interval multiplicative preference relations. Appl Soft Comput 34:60–71

    CAS  Google Scholar 

  48. Zhang HQ, Jiang W, Deng XY (2020) Data-driven multi-attribute decision-making by combining probability distributions based on compatibility and entropy. Appl Intell. https://doi.org/10.1007/s10489-020-01738-9

    Article  Google Scholar 

  49. Zhang ZM, Pedrycz W (2019) A consistency and consensus-based goal programming method for group decision-making with interval-valued intuitionistic multiplicative preference relations. IEEE Trans Cybern 49:3640–3654

    PubMed  Google Scholar 

  50. Zheng GZ, Zhu N, Tian Z, Chen Y, Sun BH (2012) Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Saf Sci 50(2):228–239

    Google Scholar 

  51. Zhou LG, Chen HY (2013) On compatibility of uncertain additive linguistic preference relations based on the linguistic COWA operator. Appl Soft Comput 13:3668–3682

    Google Scholar 

  52. Zhou LG, He YD, Chen HY, Liu JP (2014) On compatibility of interval multiplicative preference relations based on the COWGA operator. Int J Uncertain Fuzziness Knowl Based Syst 22:407–428

    MathSciNet  Google Scholar 

  53. Zhou LG, Merigó JM, Chen HY, Liu JP (2016) The optimal group continuous logarithm compatibility measure for interval multiplicative preference relations based on the COWGA operator. Inf Sci 328:250–269

    Google Scholar 

  54. Zhou YY, Cheng LH, Zhou LG, Chen HY, Ge JQ (2017) A group decision making approach for trapezoidal fuzzy preference relations with compatibility measure. Soft Comput 21:2709–2721

    Google Scholar 

  55. Zhou YY, Zhu JM, Zhou LG, Chen HY, Zheng T (2018) A new approach to fuzzy group decision making with trapezoidal fuzzy preference relations by using compatibility measure. Neural Comput Appl 29:1187–1203

    Google Scholar 

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Acknowledgements

The work was supported by National Natural Science Foundation of China (Nos.72171002, 71771001, 71701001, 71871001, 71901001, 71901088, 72071001, 72001001,72201004), Natural Science Foundation for Distinguished Young Scholars of Anhui Province (No.1908085J03), Research Funding Project of Academic and technical leaders and reserve candidates in Anhui Province (No.2018H179), Top Talent Academic Foundation for University Discipline of Anhui Province (No.gxbjZD2020056).

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Correspondence to Ligang Zhou.

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Zhou, Y., Zheng, C., Wu, P. et al. A new compatibility model for fuzzy group decision making by using trapezoidal fuzzy preference relations with COWA operator. Int. J. Mach. Learn. & Cyber. 15, 1055–1073 (2024). https://doi.org/10.1007/s13042-023-01955-x

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