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Group Decision-Making with Linguistic Cognition from a Reliability Perspective

  • Zhenzhen Ma
  • Jianjun ZhuEmail author
  • Kumaraswamy Ponnambalam
  • Ye Chen
  • Shitao Zhang
Article
  • 29 Downloads

Abstract

To deal reliably with the cognitive uncertainty experienced by decision-makers when facing problems involving linguistic group decision-making, we investigate a new research perspective: cognitive familiarity is regarded as a measure of cognitive reliability. The linguistic variables examined in this work are quantified with the use of several granulation optimization models that include consideration of cognitive reliability. Three types of linguistic variables are used for describing the alternative grades, attribute weights, and levels of cognitive familiarity associated with the experts involved. Three interrelated optimization models are built to quantify these linguistic variables. An information entropy model first determines cognitive familiarity, which is applied as a measure of cognitive reliability. Using group consistency and cognitive reliability, two proposed optimization models then successively establish the attribute weights and alternative grades. The final element is a proposed new selection method based on the aggregated values for the alternative grades and cognitive reliability. An illustrative example clarifies the steps in the proposed method, which produces a ranking of three alternatives as a final decision. The validity and advantages of the proposed method are verified through a comparison with existing approaches. The proposed method can be employed for effectively resolving decision-making uncertainty through the improvement of the group consistency and cognitive reliability of the experts. Sensitivity analysis also reveals that cognitive reliability has a strong impact on decision-making and should thus be considered during fusion processes.

Keywords

Decision analysis Cognitive reliability Group consistency Cognitive familiarity 

Notes

Funding Information

This work was funded by the National Natural Science Foundation of China (nos. 71171112 and 71502073), the Key Project of National Social Science Foundation of China (no. 14AZD049), the Scientific Innovation Research of College Graduates Jiangsu Province (no. KYZZ150094), the Foundation of the Ministry of Education (no. 14YJC630120), and the Anhui Provincial Natural Science Foundation (no. 1708085MG168).

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Research Involving Human Participants and/or Animals

This article does not describe any studies involving human participants or animals performed by any of the authors.

References

  1. 1.
    Zhao N, Xu ZS, Liu FJ. Group decision making with dual hesitant fuzzy preference relations. Cogn Comput 2016;8(6):1119–43.Google Scholar
  2. 2.
    Li J, Wang JQ. Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cogn Comput 2017; 9(5):611–25.Google Scholar
  3. 3.
    Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 1975;8 (3):199–249.Google Scholar
  4. 4.
    Abbass HA, Petraki E, Merrick K, Harvey J, Barlow M. Trusted autonomy and cognitive cyber symbiosis: open challenges. Cogn Comput 2016;8(3):385–408.Google Scholar
  5. 5.
    Liu PD, Tang GL. Multi-criteria group decision-making based on interval neutrosophic uncertainlinguistic variables and choquet integral. Cogn Comput 2016;8(6):1036–56.Google Scholar
  6. 6.
    Zhang ST, Zhu JJ, Liu XD, Chen Y, Ma ZZ. Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation. Appl Soft Comput 2017;60:30–47.Google Scholar
  7. 7.
    Zhao J, Xie XJ, Xu X, Sun SL. Multi-view learning overview: Recent progress and new challenges. Inf Fus 2017;38:43–54.Google Scholar
  8. 8.
    Xu C, Tao DC, Xu C. Multi-view learning with incomplete views. IEEE Trans Image Process 2015;24 (12):5812–25.PubMedGoogle Scholar
  9. 9.
    Meng FY, Wang C, Chen XH. Linguistic interval hesitant fuzzy sets and their application in decision making. Cogn Comput 2016;8(1):52–68.Google Scholar
  10. 10.
    Wang JQ, Cao YX, Zhang HY. Multi-criteria decision-making method based on distance measure and choquet integral for linguistic Z-numbers. Cogn Comput 2017;9(6):827–42.Google Scholar
  11. 11.
    Tian ZB, Wang J, Wang JQ, et al. A likelihood-based qualitative flexible approach with hesitant fuzzy linguistic information. Cogn Comput 2016;8(4):670–83.Google Scholar
  12. 12.
    Yan HB, Ma T. A group decision making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority. Eur J Oper Res 2015;241(3):815–29.Google Scholar
  13. 13.
    Chen ZS, Chin KS, Li YL, Yang Y. Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making. Inf Sci 2016;357:61–87.Google Scholar
  14. 14.
    Wei GW. Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Expert Syst Appl 2011;38(5):4824–8.Google Scholar
  15. 15.
    Zhang HY, Ji P, Wang JQ, Chen XH. A neutrosophic normal cloud and its application in decision-making. Cogn Comput 2016;8(4):649–69.Google Scholar
  16. 16.
    Massanet S, Vicente Riera J, Torrens J, Herrera-Viedma E. A model based on subjective linguistic preference relations for group decision making problems. Inf Sci 2016;355-356:249–64.Google Scholar
  17. 17.
    Kerr-Wilson J, Pedrycz W. Design of rule-based models through information granulation. Expert Syst Appl 2016;46:274–85.Google Scholar
  18. 18.
    Wang BL, Liang JY, Qian YH. Determining decision makersąŕ weights in group ranking: a granular computing method. Int J Mach Learn Cyber 2015;6:511–21.Google Scholar
  19. 19.
    Wu ZB, Xu JP. Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega 2016;65:28–40.Google Scholar
  20. 20.
    Farhadinia B, Xu ZS. Distance and aggregation-based methodologies for hesitant fuzzy decision making. Cogn Comput 2017;9(1):81–94.Google Scholar
  21. 21.
    Fu C, Yang JB, Yang SL. A group evidential reasoning approach based on expert reliability. Eur J Oper Res 2015;246(3):886– 93.Google Scholar
  22. 22.
    Lin GP, Liang JY, Qian YH. An information fusion approach by combining multigranulation rough sets and evidence theory. Inf Sci 2015;314:184–99.Google Scholar
  23. 23.
    Wang XD, Zhu JW, Song YF, Lei L. Combination of unreliable evidence sources in intuitionistic fuzzy MCDM framework. Knowl-Based Syst 2016;97:24–39.Google Scholar
  24. 24.
    Zhou W, Xu ZS. Generalized asymmetric linguistic term set and its application to qualitative decision making involving risk appetites. Eur J Oper Res 2016;254(2):610–21.Google Scholar
  25. 25.
    Van Horenbeek A, Pintelon L. Development of a maintenance performance measurement framework-using the analytic network process (ANP) for maintenance performance indicator selection. Omega 2014;42(1):33–46.Google Scholar
  26. 26.
    Zhu JJ, Wang HH, Chen Y, et al. Project evaluation method using non-formatted text information based on multi-granular linguistic labels. Inf Fus 2015;24:93–107.Google Scholar
  27. 27.
    Alieva RA, Witold P, Kreinovich V, et al. The general theory of decisions. Inf Sci 2016;327:125–48.Google Scholar
  28. 28.
    Mi CM, Ma ZZ, Ding ZQ. A linguistic evaluation model considering grey information and its application on complex product supplier performance. J Grey Syst 2013;25(3):34–43.Google Scholar
  29. 29.
    Herrera F, Herrera-Viedma E. Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Set Syst 2000;115(1):67–82.Google Scholar
  30. 30.
    Boumahdi F, Chalal R, Guendouz A, Gasmia K. SOA+D: a new way to design the decision in SOA-based on the new standard Decision Model and Notation (DMN). SOCA 2016;10(1):35– 53.Google Scholar
  31. 31.
    Xu ZS, Da QL. The uncertain OWA operator. Int J Intell Syst 2002;17(6):569–75.Google Scholar
  32. 32.
    Zhao KQ. The theoretical basis and basic algorithm of binary connection A+Bi and its application in AI. CAAI Trans Intell Syst 2008;3(6):476–86.Google Scholar
  33. 33.
    De Almeida AT, De Almeida JA, Costa APCS, et al. A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoff. Eur J Oper Res 2016;250(1):179–91.Google Scholar
  34. 34.
    Cover TM, Thomas JA. Elements of information theory. Hoboken: Wiley; 2006, p. 14.Google Scholar
  35. 35.
    Larson R, Calculus EB. ., 9th. Boston: Cengage Learning; 2009.Google Scholar
  36. 36.
    Wu J, Dai LF, Chiclana F, Fujita H, Herrera-Viedma E. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Inf Fus 2018;41:232–42.Google Scholar
  37. 37.
    Zeng YR, Wang L, Xu XH. An integrated model to select an ERP system for Chinese small-small medium-sized enterprise under uncertainty. Technol Econ Dev Eco 2017;23(1):38–58.Google Scholar
  38. 38.
    Khezrian M, Jahan A, Wan Kadir WMN, Ibrahim S. An approach for web service selection based on confidence level of decision maker. PLoS One 2014;9(6):e97831.PubMedPubMedCentralGoogle Scholar
  39. 39.
    Luo PF, Wang HM, Yang ZJ. Investment and financing for SMEs with a partial guarantee and jump risk. Eur J Oper Res 2016;249(3):1161–68.Google Scholar
  40. 40.
    Ju YH, Sohn SY. Stress test for a technology credit guarantee fund based on survival analysis. J Oper Res Soc 2015;66(3):463–73.Google Scholar
  41. 41.
    Lu MT, Hu SK, Huang LH, et al. Evaluating the implementation of business-to-business m-commerce by SMEs based on a new hybrid MADM model. Manag Decis 2014;53(2):290–317.Google Scholar
  42. 42.
    Boubeta-Puig J, Ortiz G, Medina-Bulo I. MEDit4CEP: A model-driven solution for real-time decision making in SOA 2.0. Knowl-Based Syst 2015;89:97–112.Google Scholar
  43. 43.
    Archimĺĺde B, Memon MA, Ishak K. Combining multi-agent model, SOA And ontologies in a distributed and interoperable architecture to manage multi-site production projects. Int J Comput Integ M 2017;30 (8):856–70.Google Scholar
  44. 44.
    Guneri AF, Gul M, Ozgurler S. A fuzzy AHP methodology for selection of risk assessment methods in occupational safety. Int J Risk Assess Manag 2015;18(3-4):319–35.Google Scholar
  45. 45.
    Singh N, Tyagi K. Ranking of services for reliability estimation of SOA system using fuzzy multicriteria analysis with similarity-based approach. Int J Syst Assur Eng Manag 2017;8:317–26.Google Scholar
  46. 46.
    Sohn SY, Moon TH, Kim S. Improved technology scoring model for credit guarantee fund. Expert Syst Appl 2005;28(2):327– 31.Google Scholar
  47. 47.
    Ju YH, Sohn SY. Updating a credit-scoring model based on new attributes without realization of actual data. Eur J Oper Res 2014;234(1):119–26.Google Scholar
  48. 48.
    Palacios-Gomez F, Lasdon L, Engquist M. Nonlinear optimization by successive linear programming. Manag Sci 1982;28(10):1106–20.Google Scholar
  49. 49.
    Zhang J, Kim NH, Lasdon L. An improved successive linear programming algorithm. Manag Sci 1985; 31(10):1312–31.Google Scholar
  50. 50.
    Lamberti L, Pappalettere C. Comparison of the numerical efficiency of different sequential linear programming based algorithms for structural optimisation problems. Comput Struct 2000;76:713–28.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zhenzhen Ma
    • 1
  • Jianjun Zhu
    • 1
    Email author
  • Kumaraswamy Ponnambalam
    • 2
  • Ye Chen
    • 1
  • Shitao Zhang
    • 3
  1. 1.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  2. 2.Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada
  3. 3.School of Mathematics & Physics Science and EngineeringAnhui University of TechnologyMa’anshanPeople’s Republic of China

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