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Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis

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  • Feifei Jin

    (School of Business, Anhui University, Hefei 230601, China
    Anhui University Center for Applied Mathematics, Anhui University, Hefei 230601, China)

  • Chang Li

    (School of Business, Anhui University, Hefei 230601, China)

  • Jinpei Liu

    (School of Business, Anhui University, Hefei 230601, China
    Anhui University Center for Applied Mathematics, Anhui University, Hefei 230601, China
    Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27695, USA)

  • Ligang Zhou

    (Anhui University Center for Applied Mathematics, Anhui University, Hefei 230601, China
    School of Mathematical Sciences, Anhui University, Hefei 230601, China)

Abstract

The development of distribution linguistic provides a new research idea for linguistic information group decision-making (GDM) problems, which is more flexible and convenient for experts to express their opinions. However, in the process of using distribution linguistic fuzzy preference relations (DLFPRs) to solve linguistic information GDM problems, there are few studies that pay attention to both internal consistency adjustment and external consensus of experts. Therefore, this study proposes a fresh decision support model based on consistency adjustment algorithm and consensus adjustment algorithm to solve GDM problems with distribution linguistic data. Firstly, we review the concept of DLFPRs to describe the fuzzy linguistic evaluation information, and then we present the multiplicative consistency of DLFPRs and a new consistency measurement method based on the distance, and investigate the consistency adjustment algorithm to ameliorate the consistency level of DLFPRs. Subsequently, the consensus degree measurement is carried out, and a new consensus degree calculation method is put forward. At the same time, the consensus degree adjustment is taken the expert cost into account to make it reach the predetermined level. Finally, a distribution linguistic fuzzy group decision making (DLFGDM) method is designed to integrate the evaluation linguistic elements and obtain the final evaluation information. A case of the evaluation of China’s state-owned enterprise equity incentive model is provided, and the validity and superiority of the proposed method are performed by comparative analysis.

Suggested Citation

  • Feifei Jin & Chang Li & Jinpei Liu & Ligang Zhou, 2021. "Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis," Mathematics, MDPI, vol. 9(19), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2457-:d:648974
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    References listed on IDEAS

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    1. Dong, Yucheng & Xu, Yinfeng & Li, Hongyi, 2008. "On consistency measures of linguistic preference relations," European Journal of Operational Research, Elsevier, vol. 189(2), pages 430-444, September.
    2. Huang, Jia & Li, Zhaojun(Steven) & Liu, Hu-Chen, 2017. "New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 302-309.
    3. Wen-Tao Guo & Van-Nam Huynh & Songsak Sriboonchitta, 2017. "A proportional linguistic distribution based model for multiple attribute decision making under linguistic uncertainty," Annals of Operations Research, Springer, vol. 256(2), pages 305-328, September.
    4. Wenyu Yu & Zhen Zhang & Qiuyan Zhong, 2021. "Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach," Annals of Operations Research, Springer, vol. 300(2), pages 443-466, May.
    5. Labella, Álvaro & Liu, Hongbin & Rodríguez, Rosa M. & Martínez, Luis, 2020. "A Cost Consensus Metric for Consensus Reaching Processes based on a comprehensive minimum cost model," European Journal of Operational Research, Elsevier, vol. 281(2), pages 316-331.
    6. Zhang, Linling & Yuan, Jinjian & Gao, Xinyu & Jiang, Dawei, 2021. "Public transportation development decision-making under public participation: A large-scale group decision-making method based on fuzzy preference relations," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    7. Satit Yodmun & Wichai Witayakiattilerd, 2016. "Stock Selection into Portfolio by Fuzzy Quantitative Analysis and Fuzzy Multicriteria Decision Making," Advances in Operations Research, Hindawi, vol. 2016, pages 1-14, July.
    8. Feifei Jin & Jinpei Liu & Ligang Zhou & Luis Martínez, 2021. "Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory," Group Decision and Negotiation, Springer, vol. 30(4), pages 813-845, August.
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