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A Consensus Model For Group Decision Making Problems With Unbalanced Fuzzy Linguistic Information

Author

Listed:
  • F. J. CABRERIZO

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

  • S. ALONSO

    (Department of Software Engineering, University of Granada, Granada 18071, Spain)

  • E. HERRERA-VIEDMA

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

Abstract

Most group decision making (GDM) problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e. using term sets that are not uniformly and symmetrically distributed. The aim of this paper is to present a consensus model for GDM problems with unbalanced fuzzy linguistic information. This consensus model is based on both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two consensus criteria, consensus degrees, and proximity measures. To do so, we use a new fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy linguistic information,1(Int. J. Intell. Syst.22(11) (2007) 1197–1214), which uses the linguistic 2-tuple model as representation base of unbalanced fuzzy linguistic information. In addition, the consensus model presents a feedback mechanism to help experts for reaching the highest degree of consensus possible. There are two main advantages provided by this consensus model. First, its ability to cope with GDM problems with unbalanced fuzzy linguistic information overcoming the problem of finding different discrimination levels in linguistic term sets. Second, it supports the consensus process automatically, avoiding the possible subjectivity that the moderator can introduce in this phase.

Suggested Citation

  • F. J. Cabrerizo & S. Alonso & E. Herrera-Viedma, 2009. "A Consensus Model For Group Decision Making Problems With Unbalanced Fuzzy Linguistic Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 109-131.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:01:n:s0219622009003296
    DOI: 10.1142/S0219622009003296
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    Citations

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    Cited by:

    1. Pang, Jifang & Liang, Jiye, 2012. "Evaluation of the results of multi-attribute group decision-making with linguistic information," Omega, Elsevier, vol. 40(3), pages 294-301.
    2. Fu, Chao & Yang, Shan-Lin, 2010. "The group consensus based evidential reasoning approach for multiple attributive group decision analysis," European Journal of Operational Research, Elsevier, vol. 206(3), pages 601-608, November.
    3. Mario Fedrizzi & Michele Fedrizzi & Ricardo Alberto Marques Pereira & Matteo Brunelli, 2010. "The dynamics of consensus in group decision making: investigating the pairwise interactions between fuzzy preferences," DISA Working Papers 1004, Department of Computer and Management Sciences, University of Trento, Italy, revised 29 Jul 2010.
    4. Chuu, Shian-Jong, 2011. "Interactive group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a supply chain," European Journal of Operational Research, Elsevier, vol. 213(1), pages 279-289, August.
    5. Yucheng Dong & Cong-Cong Li & Yinfeng Xu & Xin Gu, 2015. "Consensus-Based Group Decision Making Under Multi-granular Unbalanced 2-Tuple Linguistic Preference Relations," Group Decision and Negotiation, Springer, vol. 24(2), pages 217-242, March.
    6. Byeong Seok Ahn, 2014. "Developing Group Ordered Weighted Averaging Operator Weights for Group Decision Support," Group Decision and Negotiation, Springer, vol. 23(5), pages 1127-1143, September.
    7. Carmen De Maio & Aurelio Tommasetti & Orlando Troisi & Massimiliano Vesci & Giuseppe Fenza & Vincenzo Loia, 2016. "Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 923-948, September.
    8. Fu, Chao & Yang, Shanlin, 2012. "An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements," European Journal of Operational Research, Elsevier, vol. 223(1), pages 167-176.
    9. Fei Teng & Peide Liu & Li Zhang & Juan Zhao, 2019. "Multiple Attribute Decision-Making Methods with Unbalanced Linguistic Variables Based on Maclaurin Symmetric Mean Operators," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 105-146, January.
    10. Sun, Bingzhen & Ma, Weimin, 2015. "An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application," Omega, Elsevier, vol. 51(C), pages 83-92.
    11. Cabrerizo, Francisco Javier & Herrera-Viedma, Enrique & Pedrycz, Witold, 2013. "A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts," European Journal of Operational Research, Elsevier, vol. 230(3), pages 624-633.
    12. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    13. Xiaoxuan Hu & Yanjun Wang & Haiquan Sun & Peng Jin, 2022. "A remote sensing satellite observation scheme evaluation method based on granular computing of intuitionistic linguistic preference relation," Annals of Operations Research, Springer, vol. 316(1), pages 343-364, September.
    14. Zhang, Hengjie & Dong, Yucheng & Chiclana, Francisco & Yu, Shui, 2019. "Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design," European Journal of Operational Research, Elsevier, vol. 275(2), pages 580-598.
    15. Fu-Ling Cai & Xiuwu Liao & Kan-Liang Wang, 2012. "An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities," Annals of Operations Research, Springer, vol. 197(1), pages 87-108, August.
    16. Fu, Chao & Yang, Shanlin, 2011. "An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context," European Journal of Operational Research, Elsevier, vol. 212(1), pages 179-189, July.

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