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Approaches to Multiple Attribute Group Decision Making Under Intuitionistic Fuzzy Settings: Application of Dempster–Shafer Theory of Evidence

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Abstract

This paper studies the multiple attribute group decision-making problems in intuitionistic fuzzy settings and interval-valued intuitionistic fuzzy settings based on Dempster–Shafer theory and information entropy. First, the degrees of uncertainty of parameters are derived from the information entropy by which new kinds of Mass functions of the different parameters are constructed. The concept of degree of the conflict of evidence is proposed. Second, the weighted combination rule of Dempster–Shafer evidence is firstly introduced to fuse the experts’ Mass functions into a collective one. Finally, two examples under intuitionistic fuzzy settings and interval-valued intuitionistic fuzzy settings are presented to demonstrate the effectiveness and applicability of the proposed approach. By the way, from the two examples, we illustrate that the multiple attribute group decision making is superior and more reliable than the decision making conducted by only one decision maker.

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References

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

    Article  MATH  Google Scholar 

  2. Atanassov, K.; Gargov, G.: Interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 31(3), 343–349 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  3. Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)

    Article  MATH  Google Scholar 

  4. Li, D.F.: Multiattribute decision making method based on generalized OWA operators with intuitionistic fuzzy sets. Expert Syst. Appl. 37(12), 8673–8678 (2010)

    Article  Google Scholar 

  5. Li, D.F.: The GOWA operator based approach to multiattribute decision making using intuitionistic fuzzy sets. Math Comput Model. 53(5–6), 1182–1196 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Xu, Z.S.: Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control Decis. 22(2), 215–219 (2007)

    MathSciNet  Google Scholar 

  7. Xu, Z.S.; Chen, J.: Approach to group decision making based on interval-valued intuitionistic judgment matrices. Syst. Eng. Theory Pract. 27(4), 126–133 (2007)

    Article  Google Scholar 

  8. Xu, Z.S.; Chen, J.: On geometric aggregation over interval-valued intuitionistic fuzzy information. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 466–471 (2007)

  9. Zhao, H.; Xu, Z.S.; Ni, M.F.; Liu, S.S.: Generalized aggregation operators for intuitionistic fuzzy sets. Int. J. Intell. Syst. 25(1), 1–30 (2010)

    Article  MATH  Google Scholar 

  10. Liu, P.D.: Some Hamacher aggregation operators based on the interval-valued intuitionistic fuzzy numbers and their application to group decision making. IEEE Trans. Fuzzy syst. 22(1), 83–97 (2014)

    Article  MathSciNet  Google Scholar 

  11. Li, Y.; Wang, Y.M.; Liu, P.D.: Multiple attribute group decision-making methods based on trapezoidal fuzzy two-dimension linguistic power generalized aggregation operators. Soft Comput. 20(7), 1–16 (2016)

    Article  MATH  Google Scholar 

  12. Chen, S.M.; Huang, Z.C.: Multiattribute decision making based on interval-valued intuitionistic fuzzy values and particle swarm optimization techniques. Inf. Sci. 397–398, 206–218 (2017)

    Article  Google Scholar 

  13. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  14. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  15. Yang, Y.; Han, D.Q.: A new distance-based total uncertainty measure in the theory of belief functions. Knowl. Based Syst. 94, 114–123 (2015)

    Article  Google Scholar 

  16. Yang, J.B.: Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. Eur. J. Oper. Res. 131(1), 31–61 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  17. Yang, J.B.; Xu, D.L.: On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 32(3), 289–304 (2002)

    Article  Google Scholar 

  18. Yang, J.B.; Wang, Y.M.; Xu, D.L.; Chin, K.S.: The evidential reasoning approach for mada under both probabilistic and fuzzy uncertainties. Eur. J. Oper. Res. 171(1), 309–343 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  19. Yang, J.B.; Liu, J.; Wang, J.; Sii, H.S.; Wang, H.W.: Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 36(2), 266–285 (2006)

    Article  Google Scholar 

  20. Jiang, W.; Wei, B.Y.; Qin, X.Y.; Zhan, J.; Tang, Y.C.: Sensor data fusion based on a new conflict measure. Math. Probl. Eng. 2016, 1–11 (2016)

    MathSciNet  MATH  Google Scholar 

  21. Jiang, W.; Zhan, J.: A modified combination rule in generalized evidence theory. Appl. Intell. 46(3), 630–640 (2017)

    Article  MathSciNet  Google Scholar 

  22. Lin, G.P.; Liang, J.Y.; Qian, Y.H.: An information fusion approach by combining multigranulation rough sets and evidence theory. Inf. Sci. 314, 184–199 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  23. Liu, Z.G.; Pan, Q.; Dezert, J.; Mercier, G.: Credal C-means clustering method based on belief functions. Knowl. Based Syst. 74(1), 119–132 (2014)

    Google Scholar 

  24. Frikha, A.: On the use of a multi-criteria approach for reliability estimation in belief function theory. Inf. Fusion. 18(18), 20–32 (2014)

    Article  Google Scholar 

  25. Yager, R.R.; Alajlan, N.: Dempster-Shafer belief structures for decision making under uncertainty. Knowl. Based Syst. 80(C), 58–66 (2015)

    Article  Google Scholar 

  26. Xiao, Z.; Yang, X.L.; Niu, Q.; Dong, Y.X.; Gong, K.; Xia, S.S.; Pang, Y.: A new evaluation method based on Dempster–Shafer generalized fuzzy soft sets and its application in medical diagnosis problem. Appl. Math. Model. 36(10), 4592–4604 (2012)

    Article  MATH  Google Scholar 

  27. Li, Z.W.; Wen, G.P.; Xie, N.X.: An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster–Shafer theory of evidence: an application in medical diagnosis. Artif. Intell. Med. 64(3), 161–171 (2015)

    Article  Google Scholar 

  28. Tang, H.X.: A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster–Shafer theory of evidence. Appl. Soft Comput. 31(C), 317–325 (2015)

    Article  Google Scholar 

  29. Wang, J.W.; Hu, Y.; Xiao, F.Y.; Deng, X.Y.; Deng, Y.: A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster–Shafer theory of evidence: an application in medical diagnosis. Artif. Intell. Med. 69, 1–11 (2016)

    Article  Google Scholar 

  30. Chen, S.M.; Cheng, S.H.; Chiou, C.H.: Fuzzy multiattribute group decision making based on intuitionistic fuzzy sets and evidential reasoning methodology. Inf. Fusion. 27, 215–227 (2016)

    Article  Google Scholar 

  31. Meng, F.Y.; Tan, C.Q.; Chen, X.H.: An approach to Atanassov’s interval-valued intuitionistic fuzzy multi-attribute decision making based on prospect theory. Int. J. Comput. Intell. Syst. 8(3), 591–605 (2015)

    Article  Google Scholar 

  32. Xu, Z.S.: Intuitionistic fuzzy aggregation operators. IEEE Trans. Fuzzy Syst. 14(6), 1179–1187 (2007)

    Google Scholar 

  33. Xu, Z.S.; Yager, R.R.: Some geometric aggregation operators based on intuitionistic fuzzy sets. Int. J. Gen. Syst. 35(4), 417–433 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. Chen, S.M.; Tan, J.M.: Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst. 67(2), 163–172 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  35. Xu, Z.S.: A method for multiple attribute decision making with incomplete weight information in linguistic setting. Knowl. Based Syst. 20(8), 719–725 (2007)

    Article  Google Scholar 

  36. Jousselme, A.L.; Grenier, D.; Bossé, éloi: A new distance between two bodies of evidence. Inf. Fusion. 2(2), 91–101 (2001)

    Article  Google Scholar 

  37. Shannon, C.E.: The mathematical theory of communication. Bell Syst. Tech. J. 27(3and4), 373–423 (1948)

    MathSciNet  Google Scholar 

  38. Liang, J.Y.; Chin, K.S.; Dang, C.Y.; Richard, C.M.: A new method for measuring uncertainty and fuzziness in rough set theory. Int. J. Gen. Syst. 31(4), 331–342 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  39. Zhang, Q.: Difference Information Theory in Grey Hazy Set. PetroleumIndustry Press, Beijing (2002)

    Google Scholar 

  40. Wei, G.W.: Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making. Appl. Soft Comput. 10, 423–431 (2010)

    Article  Google Scholar 

  41. Ye, J.: Multiple attribute group decision-making methods with completely unknown weights in intuitionistic fuzzy setting and interval-valued intuitionistic fuzzy setting. Int. J. Gen. Syst. 22(5), 173–188 (2013)

    MATH  Google Scholar 

  42. Ye, J.: Multiple attribute group decision-making methods with completely unknown weights in intuitionistic fuzzy setting and interval-valued intuitionistic fuzzy setting. Int. J. Gen. Syst. 42(5), 489–502 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  43. Ye, J.: Fuzzy cross entropy of interval-valued fuzzy sets and its optimal intuitionistic fuzzy decision-making method based on the weights of alternatives. Expert Syst Appl. 38, 6179–6183 (2011)

    Article  Google Scholar 

  44. Park, D.G.; Kwun, Y.C.; Park, J.H.; Park, I.Y.: Correlation coefficient of interval-valued intuitionistic fuzzy sets and its application to multiple attribute group decision making problems. Math. Comput. Model. 50(10), 1279–1293 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  45. Yue, Z.L.: Deriving decision maker’s weights based on distance measure for interval-valued intuitionistic fuzzy group decision making. Expert Syst. Appl. 38(9), 11665–11670 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Program for The Education Department of Liaoning Province of China (Grant No. L201615).

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Correspondence to Lishi Zhang.

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Zhang, L. Approaches to Multiple Attribute Group Decision Making Under Intuitionistic Fuzzy Settings: Application of Dempster–Shafer Theory of Evidence. Arab J Sci Eng 44, 3719–3732 (2019). https://doi.org/10.1007/s13369-018-3657-5

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