IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i19p2457-d648974.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/19/2457/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/19/2457/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tiantian Gai & Mingshuo Cao & Francisco Chiclana & Zhen Zhang & Yucheng Dong & Enrique Herrera-Viedma & Jian Wu, 2023. "Consensus-trust Driven Bidirectional Feedback Mechanism for Improving Consensus in Social Network Large-group Decision Making," Group Decision and Negotiation, Springer, vol. 32(1), pages 45-74, February.
    2. María Carmen Carnero, 2020. "Waste Segregation FMEA Model Integrating Intuitionistic Fuzzy Set and the PAPRIKA Method," Mathematics, MDPI, vol. 8(8), pages 1-29, August.
    3. Yan, Hong-Bin & Ma, Tieju & Huynh, Van-Nam, 2017. "On qualitative multi-attribute group decision making and its consensus measure: A probability based perspective," Omega, Elsevier, vol. 70(C), pages 94-117.
    4. González-Arteaga, T. & Alcantud, J.C.R. & de Andrés Calle, R., 2016. "A cardinal dissensus measure based on the Mahalanobis distance," European Journal of Operational Research, Elsevier, vol. 251(2), pages 575-585.
    5. Gong, Zaiwu & Guo, Weiwei & Słowiński, Roman, 2021. "Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction," Omega, Elsevier, vol. 104(C).
    6. 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.
    7. Sheng Liu & Xiaojie Guo & Lanyong Zhang, 2019. "An Improved Assessment Method for FMEA for a Shipboard Integrated Electric Propulsion System Using Fuzzy Logic and DEMATEL Theory," Energies, MDPI, vol. 12(16), pages 1-17, August.
    8. Meimei Xia & Jian Chen & Juliang Zhang, 2015. "Multi-criteria decision making based on relative measures," Annals of Operations Research, Springer, vol. 229(1), pages 791-811, June.
    9. 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.
    10. Wu, Zhibin & Huang, Shuai & Xu, Jiuping, 2019. "Multi-stage optimization models for individual consistency and group consensus with preference relations," European Journal of Operational Research, Elsevier, vol. 275(1), pages 182-194.
    11. Mamata Sahu & Anjana Gupta, 2019. "Improving the consistency of incomplete hesitant multiplicative preference relation," OPSEARCH, Springer;Operational Research Society of India, vol. 56(1), pages 324-343, March.
    12. Faiella, Giuliana & Parand, Anam & Franklin, Bryony Dean & Chana, Prem & Cesarelli, Mario & Stanton, Neville A. & Sevdalis, Nick, 2018. "Expanding healthcare failure mode and effect analysis: A composite proactive risk analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 117-126.
    13. Huimin Xiao & Shouwen Wu & Chunsheng Cui, 2022. "The Research on Consistency Checking and Improvement of Probabilistic Linguistic Preference Relation Based on Similarity Measure and Minimum Adjustment Model," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    14. Zhi-Jiao Du & Zhi-Xiang Chen & Su-Min Yu, 2021. "Improved Failure Mode and Effect Analysis: Implementing Risk Assessment and Conflict Risk Mitigation with Probabilistic Linguistic Information," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
    15. Erick Yohanes Kalengkongan & Wilson Bogar & Fitri H. Mamonto, 2022. "The Quality of Vehicles' Public Service Testing in The Tomohon Transportation Department," Technium Social Sciences Journal, Technium Science, vol. 32(1), pages 62-75, June.
    16. Huang, Jia & You, Jian-Xin & Liu, Hu-Chen & Song, Ming-Shun, 2020. "Failure mode and effect analysis improvement: A systematic literature review and future research agenda," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    17. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching, 2019. "A novel failure mode and effect analysis model for machine tool risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 173-183.
    18. Tang, Ming & Liao, Huchang, 2024. "Group efficiency and individual fairness tradeoff in making wise decisions," Omega, Elsevier, vol. 124(C).
    19. Yuanming Li & Ying Ji & Shaojian Qu, 2022. "Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization," Group Decision and Negotiation, Springer, vol. 31(2), pages 453-489, April.
    20. Moath Alrifaey & Tang Sai Hong & Eris Elianddy Supeni & Azizan As’arry & Chun Kit Ang, 2019. "Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework," Energies, MDPI, vol. 12(4), pages 1-22, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2457-:d:648974. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.