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Group Decision-Making Based on Set Theory and Weighted Geometric Operator with Interval Rough Multiplicative Reciprocal Matrix

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Abstract

Interval rough numbers play an important role in dealing with complex fuzzy relationships. In this paper, a group decision-making (GDM) model based on interval rough multiplicative reciprocal (IRMR) matrix is proposed. Firstly, the inconsistency, satisfactory consistency and complete consistency of the IRMR matrix are defined from the perspective of set theory. Secondly, an improved method for the inconsistent IRMR matrix is introduced to address the inconsistent preference matrix in GDM. We define the uniform approximation matrix of the IRMR matrix, prove its existence, and provide a new calculation method for the sorting vector of IRMR matrix. Finally, the multiplicative reciprocal matrix obtained with a weighted geometric operator assembly is still the IRMR matrix. A GDM algorithm of the IRMR matrix is presented. The proposed algorithm is demonstrated using an illustrative example, and its feasibility and effectiveness are verified through comparison with other existing methods.

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References

  1. Kim, S.H., Choi, S.H., Kim, J.K.: An interactive procedure for multiple attribute group decision making with incomplete information: range-based approach. Eur. J. Oper. Res. 118(2), 139–152 (1999)

    Article  MATH  Google Scholar 

  2. Ning, C.G.: Consistency research of uncertain judgement matrix. Guangxi university, Nanning (2017)

    Google Scholar 

  3. Lv, Y.J., Shi, W.L., Guo, X.R.: The conditions of rank preservation and a general priority formula for fuzzy complementary judgement matrix. Math. Pract. Theory 39(15), 153–158 (2009)

    MathSciNet  MATH  Google Scholar 

  4. Gong, Z.W., Guo, W.W., Herrera-Viedma, E., Gong, Z.J., Wei, G.: Consistency and consensus modeling of linear uncertain preference relations. Eur. J. Oper. Res. 283(1), 290–307 (2020). https://doi.org/10.1016/j.ejor.2019.10.035

    Article  MathSciNet  MATH  Google Scholar 

  5. Liu, X., Xu, Y.J., Herrera, F.: Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: detecting and managing overconfidence behaviors. Inf. Fus. 52, 245–256 (2019). https://doi.org/10.1016/j.inffus.2019.03.001

    Article  Google Scholar 

  6. Xu, Z.S., Liao, H.C.: Intuitionistic Fuzzy Analytic Hierarchy Process. IEEE Trans. Fuzzy Syst. 22(4), 749–761 (2014). https://doi.org/10.1109/tfuzz.2013.2272585

    Article  Google Scholar 

  7. Nie, R.X., Wang, J.Q.: Prospect theory-based consistency recovery strategies with multiplicative probabilistic linguistic preference relations in managing group decision making. Arab. J. Sci. Eng. 45(3), 2113–2130 (2020). https://doi.org/10.1007/s13369-019-04053-9

    Article  Google Scholar 

  8. Zhang, X.Y., Zhang, H.Y., Wang, J.Q.: Discussing incomplete 2-tuple fuzzy linguistic preference relations in multi-granular linguistic MCGDM with unknown weight information. Soft. Comput. 23(6), 2015–2032 (2019)

    Article  MATH  Google Scholar 

  9. Nie, R.X., Tian, Z.P., Wang, J.Q., Luo, H.Y.: An objective and interactive-information-based feedback mechanism for the consensus-reaching process considering a nonsupport degree for minority opinions. Expert Syst. (2020). https://doi.org/10.1111/exsy.12543

    Article  Google Scholar 

  10. Liu, F., Zhang, W.G., Zhang, L.H.: A group decision making model based on a generalized ordered weighted geometric average operator with interval preference matrices. Fuzzy Sets Syst. 246, 1–18 (2014). https://doi.org/10.1016/j.fss.2013.07.010

    Article  MathSciNet  MATH  Google Scholar 

  11. Lv, Y.J., Yang, Y.H.: Analytic hierarchy process based on interval rough number. Syst. Eng. Theory Pract. 38(3), 786–793 (2018)

    Google Scholar 

  12. Gong, Z.W., Li, L.S., Forrest, J., Zhao, Y.: The optimal priority models of the intuitionistic fuzzy preference relation and their application in selecting industries with higher meteorological sensitivity. Expert Syst. Appl. 38(4), 4394–4402 (2011). https://doi.org/10.1016/j.eswa.2010.09.109

    Article  Google Scholar 

  13. Wan, S.P., Wang, F., Dong, J.Y.: A three-phase method for group decision making with interval-valued intuitionistic fuzzy preference relations. IEEE Trans. Fuzzy Syst. 26(2), 998–1010 (2018). https://doi.org/10.1109/tfuzz.2017.2701324

    Article  Google Scholar 

  14. Nie, R.X., Wang, J.Q., Wang, T.L.: A three-cycle decision-making selection mechanism with intuitionistic trapezoidal fuzzy preference relations. J. Intell. Fuzzy Syst. 36(6), 5409–5422 (2019). https://doi.org/10.3233/jifs-181306

    Article  Google Scholar 

  15. Qazi, K.I., Lam, H.K., Xiao, B., Ouyang, G., Yin, X.: Classification of epilepsy using computational intelligence techniques. CAAI Trans. Intell. Technol. 1(2), 137–149 (2016). https://doi.org/10.1016/j.trit.2016.08.001

    Article  Google Scholar 

  16. Li, J., Wang, J.Q., Hu, J.H.: Consensus building for hesitant fuzzy preference relations with multiplicative consistency. Comput. Ind. Eng. 128, 387–400 (2019). https://doi.org/10.1016/j.cie.2018.12.051

    Article  Google Scholar 

  17. Zhu, B., Xu, Z.S., Xu, J.P.: Deriving a ranking from hesitant fuzzy preference relations under group decision making. IEEE Trans. Cybern. 44(8), 1328–1337 (2014). https://doi.org/10.1109/TCYB.2013.2283021

    Article  Google Scholar 

  18. Pawlak, Z.: Rough set approach to knowledge-based decision support. Eur. J. Oper. Res. 99(1), 48–57 (1997)

    Article  MATH  Google Scholar 

  19. Pamučar, D., Mihajlović, M., Obradović, R., Atanasković, P.: Novel approach to group multi-criteria decision making based on interval rough numbers: hybrid DEMATEL-ANP-MAIRCA model. Expert Syst. Appl. 88, 58–80 (2017). https://doi.org/10.1016/j.eswa.2017.06.037

    Article  Google Scholar 

  20. Pamučar, D., Stević, Ž., Zavadskas, E.K.: Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Appl. Soft Comput. 67, 141–163 (2018). https://doi.org/10.1016/j.asoc.2018.02.057

    Article  Google Scholar 

  21. Kong, Z.J., Zhao, X.T., Li, B., Geng, L.S.: Interval rough number DEMATEL decision method considering different expert preferences. Manuf. Autom. 41(5), 66–69 (2019)

    Google Scholar 

  22. Tian, C., Peng, J.J., Zhang, W.Y., Zhang, S., Wang, J.Q.: Tourism environmental impact assessment based on improved AHP and picture fuzzy PROMETHEE II methods. Technol. Econ. Dev. Econ. 26(2), 355–378 (2019). https://doi.org/10.3846/tede.2019.11413

    Article  Google Scholar 

  23. Wei, X.J.: Obtaining weight vector of interval number judgment matrix based on preference information and its application in agricultural internet of things. Revista de la Facultad de Agronomia de la Universidad del Zulia. 36(1), 140–149 (2019)

    Google Scholar 

  24. Liang, R.X., Wang, J.Q., Zhang, H.Y.: A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information. Neural Comput. Appl. 30(11), 3383–3398 (2018). https://doi.org/10.1007/s00521-017-2925-8

    Article  Google Scholar 

  25. Yue, Q., Fan, Z.P.: Consistency analysis and ranking method for interval reciprocal judgement matrices. J. Syst. Eng. 25(4), 459–466 (2010)

    MATH  Google Scholar 

  26. Fan, Q.H., Liu, B.X., Zhang, Y.H., Zhou, R.Y.: An algorithm of improving the consistence of the positive reciprocal matrix based on relative error. In: International Conference on Information Computing and Applications. Springer, Berlin, Heidelberg. 12(1), 177–183 (2011)

  27. Meng, F.Y., Zeng, X.L.: A new method for judgement matrix consistency rectification. Stat. Decis. 15(3), 134–135 (2007). https://doi.org/10.13546/j.cnki.tjyjc.2007.15.035

    Article  Google Scholar 

  28. Liu, T.C., Li, Z.F.: Improved method of inconsistent interval reciprocal judgment matrix. Fuzzy Syst. Math. 31(4), 117–123 (2017)

    MATH  Google Scholar 

  29. Wang, X.H., Qin, X.Z., Yang, D.L.: The pattern recognition method for correcting the judgment matrix into one with complete uniformity in AHP. Syst. Eng. Theory Pract. 17(11), 56–59 (1997)

    Google Scholar 

  30. Liu, W.L., Lei, Z.J.: Study on rectification method for the judgment matrix in AHP. Syst. Eng. Theory Pract. 17(6), 30–34 (1997)

    Google Scholar 

  31. Arbel, A.: Approximate articulation of preference and priority derivation. Eur. J. Oper. Res. 43(1), 317–326 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  32. Wei, C.P., Zhang, Y.Z., Feng, X.Q.: Deriving weights from interval comparison matrics based on consistency test. Syst. Eng. Theory Pract. 27(10), 132–139 (2007)

    Article  Google Scholar 

  33. Mikhailov, L.: A fuzzy approach to deriving priorities from interval pairwise comparison judgements. Eur. J. Oper. Res. 159(3), 687–704 (2004). https://doi.org/10.1016/s0377-2217(03)00432-6

    Article  MathSciNet  MATH  Google Scholar 

  34. Yang, Q., Du, P.A., Wang, Y., Liang, B.: Developing a rough set based approach for group decision making based on determining weights of decision makers with interval numbers. Oper. Res. Int. J. 18(3), 757–779 (2017). https://doi.org/10.1007/s12351-017-0344-3

    Article  Google Scholar 

  35. Yue, Z.L.: A method for group decision-making based on determining weights of decision makers using TOPSIS. Appl. Math. Model. 35(4), 1926–1936 (2011). https://doi.org/10.1016/j.apm.2010.11.001

    Article  MathSciNet  MATH  Google Scholar 

  36. Abootalebi, S., Hadi-Vencheh, A., Jamshidi, A.: An Improvement to determining expert weights in group multiple attribute decision making problem. Group Decis. Negot. 27(2), 215–221 (2018). https://doi.org/10.1007/s10726-018-9555-0

    Article  Google Scholar 

  37. Alias, F.M.A., Abdullah, L., Gou, X.J., Liao, H.C., Herrera-Viedma, E.: Consistent fuzzy preference relation with geometric Bonferroni mean: a fused preference method for assessing the quality of life. Appl. Intell. 49(7), 2672–2683 (2019). https://doi.org/10.1007/s10489-019-01415-6

    Article  Google Scholar 

  38. Li, J., Zhang, Y.L.: A novel method for aggregating interval multiplicative comparison matrices and its application in ranking alternatives. J. Intell. Fuzzy Syst. 35(3), 3675–3684 (2018). https://doi.org/10.3233/jifs-18455

    Article  Google Scholar 

  39. Tian, Z.P., Nie, R.X., Wang, J.Q., Zhang, H.Y.: A two-fold feedback mechanism to support consensus-reaching in social network group decision-making. Knowl. Based Syst. 162, 74–91 (2018). https://doi.org/10.1016/j.knosys.2018.09.030

    Article  Google Scholar 

  40. Zhang, X.Y., Wang, X.K., Yu, S.M., Wang, J.Q., Wang, T.L.: Location selection of offshore wind power station by consensus decision framework using picture fuzzy modelling. J. Clean. Prod. 202, 980–992 (2018). https://doi.org/10.1016/j.jclepro.2018.08.172

    Article  Google Scholar 

  41. Tian, Z.P., Nie, R.X., Wang, J.Q.: Social network analysis-based consensus-supporting framework for large-scale group decision-making with incomplete interval type-2 fuzzy information. Inf. Sci. 502, 446–471 (2019). https://doi.org/10.1016/j.ins.2019.06.053

    Article  Google Scholar 

  42. Meng, F.Y., Tang, J., An, Q.X., Chen, X.H.: Decision making with intuitionistic linguistic preference relations. Int. Trans. Oper. Res. 26(5), 2004–2031 (2017). https://doi.org/10.1111/itor.12383

    Article  MathSciNet  Google Scholar 

  43. Zeng, L., Zeng, X.Y.: Research on a class of multiple attribute decision making method with interval rough numbers. Control Decis. 25(11), 1757–1760 (2010). https://doi.org/10.13195/j.cd.2010.11.159.zengl.028

    Article  MathSciNet  Google Scholar 

  44. Weng, S.Z., Lv, Y.J.: Sorting method with interval rough number and its application. J. Nanjing Univ. 51(4), 818–825 (2015)

    Google Scholar 

  45. Xia, X.D., Lv, Y.J.: Method based on MADM of interval rough numbers with parameters. Comput. Eng. Appl. 53(5), 255–259 (2017). https://doi.org/10.3778/j.issn.1002-8331.1608-0022

    Article  Google Scholar 

  46. Xie, F.P., Zeng, X.L., Duan, Y.Y.: Priority method for complementary judgement matrix based on interval rough number. J. Qiongzhou Univ. 22(5), 22–26 (2015)

    Google Scholar 

  47. He, C.L.: The study on some problems of multi-attribute decision making that based on interval rough numbers. Guangxi University, Nanning (2014)

    Google Scholar 

  48. Wei, L.Y.: The rectification method and sequencing algorithm of the inconsistent interval number judgment matrix. J. Guangxi Univ. Natl. 3(2), 1–4 (2003)

    Google Scholar 

  49. Huang, R.L., Tian, Z.J., Lv, Y.J.: Research on the consistency of interval rough number reciprocal judgment matrix. Fuzzy Syst. Math. 33(4), 124–133 (2019)

    MATH  Google Scholar 

  50. Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Trans. Knowl. Data Eng. 12(2), 331–336 (2000)

    Article  Google Scholar 

  51. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill Inc, Pennsylvania (1980)

    MATH  Google Scholar 

  52. Liu, B.D.: Theory and Practice of Uncertain Programming, vol. 5, 21st edn, pp. 64–66. Springer, Berlin Heidelberg (2009)

    Book  Google Scholar 

  53. Jin, Z.W., Guo, H.: Research on multiple attribute decision making method based on ideal point with interval rough numbers. J. Chongqing Univ. Technol. 27(5), 114–117 (2013)

    MathSciNet  Google Scholar 

  54. Qian, W.Y., Zeng, Z.: Method for ranking interval rough numbers based on possibility degree. Oper. Res. Manag. Sci. 22(1), 71–76 (2013)

    Google Scholar 

  55. Wang, L.F., Xu, S.B.: Introduction to Analytic Hierarchy Process. Renmin University of China, Beijing (1990)

    Google Scholar 

  56. Rudin, W.: Principles of Mathematical Analysis. McGraw-hill, New York (1964)

    MATH  Google Scholar 

  57. Wei, Z.Z., Wei, L.Y.: The consistent interval number judgement matrix and its characteristics. J. Guangxi Univ. Technol. 11(4), 17–20 (2000)

    Google Scholar 

  58. Meng, F.Y., An, Q.X., Tan, C.Q., Chen, X.H.: An approach for group decision making with interval fuzzy preference relations based on additive consistency and consensus analysis. IEEE Trans. Syst. Man Cybern. Syst. 47(8), 2069–2082 (2017)

    Article  Google Scholar 

  59. Tu, Z.K.: Some Properties of Intuitionistic Judgment Matrix and Interval Complementary Judgment Matrix. Hefei University of Technology, Hefei (2015)

    Google Scholar 

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Acknowledgements

We would like to thank the editors and anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 71871228).

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Correspondence to Jian-qiang Wang.

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Huang, Rl., Zhang, Hy., Peng, Jj. et al. Group Decision-Making Based on Set Theory and Weighted Geometric Operator with Interval Rough Multiplicative Reciprocal Matrix. Int. J. Fuzzy Syst. 22, 1815–1831 (2020). https://doi.org/10.1007/s40815-020-00900-2

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