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Dual hesitant fuzzy group decision making method and its application to supplier selection

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Abstract

The concept of dual hesitant fuzzy set arising from hesitant fuzzy set is generalized by including a function reflecting the decision maker’s fuzziness about the non-membership degree of the information provided. This paper studies some dual hesitant fuzzy information aggregation operators for aggregating dual hesitant fuzzy elements, such as dual hesitant fuzzy Heronian mean operator and dual hesitant fuzzy geometric Heronian mean operator. The research resulting dual hesitant fuzzy information aggregation operators finds an important role in group decision making (GDM) applications. It can fusion the experts’ opinion to the comprehensive ones and based on which an optimal decision making scheme can be determined. The properties of the proposed operators are studied and the application on GDM are investigated. The effectiveness of the GDM method is demonstrated on the case study about supplier selection.

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References

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

    Article  MathSciNet  MATH  Google Scholar 

  2. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MathSciNet  MATH  Google Scholar 

  3. Zhang Z, Wang M, Hu Y, Yang J, Ye Y, Li Y (2013) A dynamic interval-valued intuitionistic fuzzy sets applied to pattern recognition. Math Probl Eng 2013(408012):16

    MathSciNet  MATH  Google Scholar 

  4. Papakostas GA, Hatzimichailidis AG, Kaburlasos VG (2013) Distance and similarity measures between intuitionistic fuzzy sets: a comparative analysis from a pattern recognition point of view. Pattern Recognit Lett 34(14):1609–1622

    Article  Google Scholar 

  5. Boran FE, Akay D (2014) A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition. Inf Sci 255:45–57

    Article  MathSciNet  MATH  Google Scholar 

  6. Wei GW, Zhao XF (2012) Some induced correlated aggregating operators with intuitionistic fuzzy information and their application to multiple attribute group decision making. Expert Syst Appl 39(2):2026–2034

    Article  Google Scholar 

  7. Tao F, Zhao D, Zhang L (2010) Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowl Inf Syst 25(1):185–208

    Article  Google Scholar 

  8. Wei GW, Wang HJ, Lin R (2011) Application of correlation coefficient to interval-valued intuitionistic fuzzy multiple attribute decision-making with incomplete weight information. Knowl Inf Syst 26(2):337–349

    Article  Google Scholar 

  9. Tan CQ, Chen XH (2010) Intuitionistic fuzzy Choquet integral operator for multi-criteria decision making. Expert Syst Appl 37(1):149–157

    Article  Google Scholar 

  10. He YD, Chen HY, Zhou LG, Han B, Zhao Q, Liu J (2014) Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making. Expert Syst Appl 41(5):2484–2495

    Article  Google Scholar 

  11. Torra V, Narukawa Y (2009). On hesitant fuzzy sets and decision. In: IEEE international conference on fuzzy systems, 2009. FUZZ-IEEE 2009, pp 1378–1382

  12. Dubois DJ (1980) Fuzzy sets and systems: theory and applications (Vol. 144). Academic press, New York

    Google Scholar 

  13. Miyamoto S (2005) Remarks on basics of fuzzy sets and fuzzy multisets. Fuzzy Sets Syst 156(3):427–431

    Article  MathSciNet  MATH  Google Scholar 

  14. Yager RR (1986) On the theory of bags. Int J Gen Syst 13(1):23–37

    Article  MathSciNet  Google Scholar 

  15. Miyamoto S. (2000). Multisets and fuzzy multisets. In Soft computing and human-centered machines (pp. 9-33). Springer, Tokyo

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

    MATH  Google Scholar 

  17. Xu ZS, Xia MM (2011) On distance and correlation measures of hesitant fuzzy information. Int J Intell Syst 26(5):410–425

    Article  MATH  Google Scholar 

  18. Xia MM, Xu ZS (2011) Hesitant fuzzy information aggregation in decision making. Int J Approx Reason 52(3):395–407

    Article  MathSciNet  MATH  Google Scholar 

  19. Xia MM, Xu ZS, Chen N (2013) Some hesitant fuzzy aggregation operators with their application in group decision making. Group Decis Negot 22(2):259–279

    Article  Google Scholar 

  20. Zhu B, Xu ZS, Xia MM (2012) Hesitant fuzzy geometric Bonferroni means. Inf Sci 205:72–85

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhu B, Xu ZS (2014) Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Trans Fuzzy Syst 22(1):35–45

    Article  Google Scholar 

  22. Zhu B, Xu ZS (2013) Hesitant fuzzy Bonferroni means for multi-criteria decision making. J Oper Res Soc 64(12):1831–1840

    Article  Google Scholar 

  23. Zhu B, Xu ZS, Xia MM (2012) Dual hesitant fuzzy sets. J Appl Math. 879629:13. doi:10.1155/2012/879629

    MathSciNet  MATH  Google Scholar 

  24. Chen Y, Peng X, Guan G, Jiang H (2014) Approaches to multiple attribute decision making based on the correlation coefficient with dual hesitant fuzzy information. J Intell Fuzzy Syst 26(5):2547–2556

    MathSciNet  MATH  Google Scholar 

  25. Zhu B, Xu ZS (2014) Some results for dual hesitant fuzzy sets. J Intell Fuzzy Syst 26:1657–1668

    MathSciNet  MATH  Google Scholar 

  26. Farhadinia B (2014) Correlation for dual hesitant fuzzy sets and dual interval-valued hesitant fuzzy sets. Int J Intell Syst 29(2):184–205

    Article  Google Scholar 

  27. Wang L, Ni M, Zhu L (2013) Correlation measures of dual hesitant fuzzy sets. J Appl Math 593739:12

    Google Scholar 

  28. Ye J (2014) Correlation coefficient of dual hesitant fuzzy sets and its application to multiple attribute decision making. Appl Math Model 38(2):659–666

    Article  MathSciNet  Google Scholar 

  29. Wang H, Zhao XF, Wei GW (2014) Dual hesitant fuzzy aggregation operators in multiple attribute decision making. J Intell Fuzzy Syst 26(5):2281–2290

    MathSciNet  MATH  Google Scholar 

  30. Beliakov G, Pradera A, Calvo T (2007) Aggregation functions: a guide for practitioners. Springer, Berlin

    MATH  Google Scholar 

  31. Yu DJ (2013) Intuitionistic fuzzy geometric Heronian mean aggregation operators. Appl Soft Comput 13(2):1235–1246

    Article  Google Scholar 

  32. Yu DJ, Wu YY (2012) Interval-valued intuitionistic fuzzy Heronian mean operators and their application in multi-criteria decision making. Afr J Bus Manag 6(11):4158–4168

    Google Scholar 

  33. Bonferroni C (1950) Sulle medie multiple di potenze. Bollettino dell’Unione Matematica Italiana 5(3–4):267–270

    MathSciNet  MATH  Google Scholar 

  34. Yager RR (2009) On generalized Bonferroni mean operators for multi-criteria aggregation. Int J Approx Reaso 50(8):1279–1286

    Article  MathSciNet  MATH  Google Scholar 

  35. Xu ZS, Yager RR (2011) Intuitionistic fuzzy Bonferroni means. IEEE Trans Syst Man Cybern B Cybern 41(2):568–578

    Article  Google Scholar 

  36. Xia MM, Xu ZS, Zhu B (2012) Generalized intuitionistic fuzzy Bonferroni means. Int J Intell Syst 27(1):23–47

    Article  Google Scholar 

  37. Xia M, Xu Z, Zhu B (2013) Geometric Bonferroni means with their application in multi-criteria decision making. Knowl Based Syst 40:88–100

    Article  Google Scholar 

  38. Gau WL, Buehrer DJ (1993) Vague sets. IEEE Trans Syst Man Cybern 23(2):610–614

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  40. Atanassov KT, Gargov G (1989) Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst 31(3):343–349

    Article  MathSciNet  MATH  Google Scholar 

  41. Chen SJ, Chen SM (2005) Fuzzy information retrieval based on geometric-mean averaging operators. Comput Math Appl 49(7):1213–1231

    Article  MathSciNet  MATH  Google Scholar 

  42. Xu ZS, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35(4):417–433

    Article  MathSciNet  MATH  Google Scholar 

  43. Wang WZ, Liu XW (2011) Intuitionistic fuzzy geometric aggregation operators based on Einstein operations. Int J Intell Syst 26(11):1049–1075

    Article  Google Scholar 

  44. Yu DJ (2012) Group decision making based on generalized intuitionistic fuzzy prioritized geometric operator. Int J Intell Syst 27(7):635–661

    Article  Google Scholar 

  45. Zhang ZM (2013) Generalized Atanassov’s intuitionistic fuzzy power geometric operators and their application to multiple attribute group decision making. Inf Fus 14(4):460–486

    Article  Google Scholar 

  46. Wang WZ, Liu XW (2013) The multi-attribute decision making method based on interval-valued intuitionistic fuzzy Einstein hybrid weighted geometric operator. Comput Math Appl 66(10):1845–1856

    Article  MathSciNet  Google Scholar 

  47. Yu DJ (2013) Decision making based on generalized geometric operator under interval-valued intuitionistic fuzzy environment. J Intell Fuzzy Syst 25(2):471–480

    MathSciNet  MATH  Google Scholar 

  48. Wang XZ, Dong CR (2009) Improving generalization of fuzzy IF–THEN rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567

    Article  Google Scholar 

  49. Wang XZ, Dong LC, Yan JH (2012) Maximum ambiguity-based sample selection in fuzzy decision tree induction. IEEE Trans Knowl Data Eng 24(8):1491–1505

    Article  Google Scholar 

  50. Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz WA (2014) Study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst. doi:10.1109/TFUZZ.2014.2371479

    Google Scholar 

  51. Chu TC, Varma R (2012) Evaluating suppliers via a multiple levels multiple criteria decision making method under fuzzy environment. Comput Ind Eng 62(2):653–660

    Article  Google Scholar 

  52. Dalalah D, Hayajneh M, Batieha F (2011) A fuzzy multi-criteria decision making model for supplier selection. Expert Syst Appl 38(7):8384–8391

    Article  Google Scholar 

  53. Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36(8):11363–11368

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to thank the anonymous reviewers and Editor-in-Chief for their constructive comments on this study. This work has been supported by China National Natural Science Foundation (No. 71301142), Zhejiang Science & Technology Plan of China (2015C33024), Zhejiang Provincial Natural Science Foundation of China (No. LQ13G010004), Project Funded by China Postdoctoral Science Foundation (No. 2014M550353) and the National Education Information Technology Research (No. 146242069).

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Authors and Affiliations

  1. School of Economics and Management, Fuzhou University, Fuzhou, 350108, Fujian, China

    Dejian Yu & Deng-Feng Li

  2. School of Information, Zhejiang University of Finance and Economics, Hangzhou, 310018, Zhejiang, China

    Dejian Yu

  3. Manchester Business School, University of Manchester, Booth Street West, Manchester, M15 6PB, UK

    José M. Merigó

  4. Department of Management Control and Information Systems, University of Chile, Av. Diagonal Paraguay 257, 8330015, Santiago, Chile

    José M. Merigó

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  1. Dejian Yu
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  2. Deng-Feng Li
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  3. José M. Merigó
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Correspondence to Deng-Feng Li.

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Yu, D., Li, DF. & Merigó, J.M. Dual hesitant fuzzy group decision making method and its application to supplier selection. Int. J. Mach. Learn. & Cyber. 7, 819–831 (2016). https://doi.org/10.1007/s13042-015-0400-3

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