Skip to main content
Log in

Belief structure-based induced aggregation operators in decision making with hesitant fuzzy information

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, the decision-making problems under hesitant fuzzy environment are investigated with Dempster–Shafer (D–S) theory. Firstly, some basic concepts of hesitant fuzzy sets, generalized hesitant fuzzy sets and D–S theory are introduced. Then two new aggregation operators: belief structure hesitant fuzzy induced ordered weighted averaging operator and belief structure hesitant fuzzy induced ordered weighted geometric operator are developed, and some properties of the proposed operators are studied. A belief structure hesitant fuzzy induced aggregation operators-based decision-making approach under uncertainty is developed. Then the proposed operators and procedures are extended to generalized hesitant fuzzy environment. Finally, some illustrative examples show the feasibility of the proposed methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen SJ, Hwang CL, Beckmann MJ et al (1992) Fuzzy multiple attribute decision making: methods and applications. Springer, New York

    Google Scholar 

  2. Salvatore G (2001) Rough set theory for multicriterial decision analysis. Eur J Oper Res 129:1–47

    Google Scholar 

  3. Srivastava RP (2011) An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives. Int J Account Inf Syst 12:126–135

    Google Scholar 

  4. Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38:325–339

    MathSciNet  MATH  Google Scholar 

  5. Shafer GA (1976) A mathematical theory of evidence. Princeton University Press, Princeton

    MATH  Google Scholar 

  6. Baraldi P, Compare M, Zio E (2013) Maintenance policy performance assessment in presence of imprecision based on Dempster–Shafer theory of evidence. Inf Sci 245:112–131

    MATH  Google Scholar 

  7. Casanovas M, Merigó JM (2012) Fuzzy aggregation operators in decision making with Dempster–Shafer belief structure. Expert Syst Appl 39:7138–7149

    Google Scholar 

  8. Deng XY, Li Y, Deng Y (2012) A group decision making method based on Dempster–Shafer theory of evidence and IOWA operator. J Comput Inf Syst 8:3929–3936

    Google Scholar 

  9. Liu ZG, Pan Q, Dezert J (2014) Classification of uncertain and imprecise data based on evidence theory. Neurocomputing 133:459–470

    Google Scholar 

  10. Mora B, Wulder MA, White JC (2013) An approach using Dempster–Shafer theory to fuse spatial data and satellite image derived crown metrics for estimation of forest stand leading species. Inf Fusion 14:384–395

    Google Scholar 

  11. Naeini MP, Moshiri B, Araabi BN et al (2014) Learning by abstraction: hierarchical classification model using evidential theoretic approach and Bayesian ensemble model. Neurocomputing 130:73–82

    Google Scholar 

  12. Nusrat E, Yamada K (2013) A descriptive decision-making model under uncertainty: combination of Dempster–Shafer theory and prospect theory. Int J Uncertain Fuzziness Knowl-Based Syst 21:79–102

    MATH  Google Scholar 

  13. Sarabi-Jamab A, Araabi BN, Augustin T (2013) Information-based dissimilarity assessment in Dempster–Shafer theory. Knowl-Based Syst 54:114–127

    Google Scholar 

  14. Wu H, Li B, Pei YJ et al (2014) Unsupervised author disambiguation using Dempster–Shafer theory. Scientometrics 101:1955–1972

    Google Scholar 

  15. Yang W, Chen Z (2012) The quasi-arithmetic intuitionistic fuzzy OWA operators. Knowl-Based Syst 27:219–233

    Google Scholar 

  16. Yang W, Pang YF (2014) The quasi-arithmetic triangular fuzzy OWA operator based on Dempster–Shafer theory. J Intell Fuzzy Syst 26:1123–1135

    MathSciNet  MATH  Google Scholar 

  17. Merigó JM, Casanovas M (2009) Induced aggregation operators in decision making with the Dempster–Shafer belief structure. Int J Intell Syst 24:934–954

    MATH  Google Scholar 

  18. Merigó JM, Casanovas M, Martínez L (2010) Linguistic aggregation operators for linguistic decision making based on the Dempster–Shafer theory of evidence. Int J Uncertain Fuzziness Knowl-Based Syst 18:287–304

    MathSciNet  MATH  Google Scholar 

  19. Merigó JM, Casanovas M (2012) Decision making with uncertain aggregation operators using the Dempster–Shafer belief structure. Int J Innov Comput I 8:1037–1061

    Google Scholar 

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

    MATH  Google Scholar 

  21. Qian G, Wang H, Feng XQ (2013) Generalized hesitant fuzzy sets and their application in decision support system. Knowl-Based Syst 37:357–365

    Google Scholar 

  22. Qin JD, Liu XW, Pedrycz W (2016) Frank aggregation operators and their application to hesitant fuzzy multiple attribute decision making. Appl Soft Comput 41:428–452

    Google Scholar 

  23. Wang H, Xu ZS (2016) Admissible orders of typical hesitant fuzzy elements and their application in ordered information fusion in multi-criteria decision making. Inf Fusion 29:98–104

    Google Scholar 

  24. Hu BQ (2016) Three-way decision spaces based on partially ordered sets and three-way decisions based on hesitant fuzzy sets. Knowl-Based Syst 91:16–31

    Google Scholar 

  25. Joshi D, Kumar S (2016) Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making. Eur J Oper Res 248:183–191

    MathSciNet  MATH  Google Scholar 

  26. Tyagi SK (2015) Correlation coefficient of dual hesitant fuzzy sets and its applications. Appl Math Model 39:7082–7092

    MathSciNet  Google Scholar 

  27. Mu ZM, Zeng SZ, Baležentis T (2015) A novel aggregation principle for hesitant fuzzy elements. Knowl-Based Syst 84:134–143

    Google Scholar 

  28. Liao HC, Xu ZS, Zeng XJ (2015) Novel correlation coefficients between hesitant fuzzy sets and their application in decision making. Knowl-Based Syst 82:115–127

    Google Scholar 

  29. Wang JQ, Wu JT, Wang J et al (2014) Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision making problems. Inf Sci 288:55–72

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  31. Chen N, Xu ZS (2015) Hesitant fuzzy ELECTRE II approach: a new way to handle multi-criteria decision making problems. Inf Sci 292:175–197

    Google Scholar 

  32. Tan CQ, Yi WT, Chen XH (2015) Hesitant fuzzy Hamacher aggregation operators for multicriteria decision making. Appl Soft Comput 26:325–349

    Google Scholar 

  33. Farhadinia B (2014) A series of score functions for hesitant fuzzy sets. Inf Sci 277:102–110

    MathSciNet  MATH  Google Scholar 

  34. Zhang XL, Xu ZS (2014) The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment. Knowl-Based Syst 61:48–58

    Google Scholar 

  35. Zhang ZM, Wang C, Tian DZ et al (2014) Induced generalized hesitant fuzzy operators and their application to multiple attribute group decision making. Comput Ind Eng 67:116–138

    Google Scholar 

  36. Quirós P, Alonso P, Bustince H et al (2015) An entropy measure definition for finite interval-valued hesitant fuzzy sets. Knowl-Based Syst 84:121–133

    Google Scholar 

  37. Alcantud JCR, de Andrés Calle R, Torrecillas MJM (2016) Hesitant fuzzy worth: an innovative ranking methodology for hesitant fuzzy subsets. Appl Soft Comput 38:232–243

    Google Scholar 

  38. Ren ZL, Xu ZS, Wang H (2018) Multi-criteria group decision-making based on quasi-order for dual hesitant fuzzy sets and professional degrees of decision makers. Appl Soft Comput 71:20–35

    Google Scholar 

  39. Zhang ZM (2017) Multi-criteria decision-making using interval-valued hesitant fuzzy QUALIFLEX methods based on a likelihood-based comparison approach. Neural Comput Appl 28:1835–1854

    Google Scholar 

  40. Yang W, Pang YF, Shi JR et al (2018) Linguistic hesitant intuitionistic fuzzy decision-making method based on VIKOR. Neural Comput Appl 29:613–626

    Google Scholar 

  41. Wu ZB, Xu JP (2018) A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Inf Fusion 41:217–231

    Google Scholar 

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

    MATH  Google Scholar 

  43. Yager RR, Filev DP (1999) Induced ordered weighted averaging operators. IEEE Trans Syst Man Cybern 29:141–150

    Google Scholar 

  44. Chiclana F, Herrera-Viedma E, Herrera F et al (2004) Induced ordered weighted geometric operators and their use in the aggregation of multiplicative preference relations. Int J Intell Syst 19:233–255

    MATH  Google Scholar 

  45. Chiclana F, Herrera-Viedma E, Herrera F et al (2007) Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. Eur J Oper Res 182:383–399

    MATH  Google Scholar 

  46. Herrera-Viedma E, Alonso S, Chiclana F et al (2007) A consensus model for group decision making with incomplete fuzzy preference relations. IEEE Trans Fuzzy Syst 15:863–877

    MATH  Google Scholar 

  47. Merigó J, Gil-Lafuente AM (2009) The induced generalized OWA operator. Inform Sci 179:729–741

    MathSciNet  MATH  Google Scholar 

  48. Tan CQ, Chen XH (2010) Induced Choquet ordered averaging operator and its application to group decision making. Int J Intell Syst 25:59–82

    MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  51. Xu ZS, Xia MM (2011) Induced generalized intuitionistic fuzzy operators. Knowl-Based Syst 24:197–209

    Google Scholar 

  52. Zhou LG, Chen HY (2013) The induced linguistic continuous ordered weighted geometric operator and its application to group decision making. Comput Ind Eng 66:222–232

    Google Scholar 

  53. Zeng SZ, Merigo JM, Palacios-Marques D (2017) Intuitionistic fuzzy induced ordered weighted averaging distance operator and its application to decision making. J Intell Fuzzy Syst 32:11–22

    MATH  Google Scholar 

  54. Zeng SZ, Llopis-Albert C, Zhang YH (2018) A novel induced aggregation method for intuitionistic fuzzy set and its application in multiple attribute group decision making. Int J Intell Syst 33:2175–2188

    Google Scholar 

  55. XuY Cabrerizo FJ, Herrera-Viedma E (2018) A consensus model for hesitant fuzzy preference relations and its application in water allocation management. Appl Soft Comput 58:265–284

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  57. Cobb BR, Shenoy BP (2006) On the plausibility transformation method for translating belief function models to probability models. Int J Approx Reason 41:314–330

    MathSciNet  MATH  Google Scholar 

  58. Yager RR (2011) On the fusion of imprecise uncertainty measures using belief structures. Inf Sci 181:3199–3209

    MathSciNet  MATH  Google Scholar 

  59. Cholvy L (2012) Non-exclusive hypotheses in Dempster–Shafer theory. Int J Approx Reason 53:493–501

    MathSciNet  MATH  Google Scholar 

  60. Xu ZS (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20:843–865

    MATH  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Sciences Foundation of China (Nos. 71401184, 91846301, 71502178), Major Project for National Natural Science Foundation of China (No. 71790615), Key Project of Philosophy and Social Sciences Research, Ministry of Education PRC (No. 16JZD013) and China Postdoctoral Science Foundation (No. 2014M552169).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xihua Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Chen, X. Belief structure-based induced aggregation operators in decision making with hesitant fuzzy information. Neural Comput & Applic 31, 8917–8929 (2019). https://doi.org/10.1007/s00521-018-3947-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3947-6

Keywords

Navigation