IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v30y2021i2d10.1007_s10726-020-09660-8.html
   My bibliography  Save this article

Dynamic Expert Reliability Based Feedback Mechanism in Consensus Reaching Process with Distributed Preference Relations

Author

Listed:
  • Min Xue

    (Hefei University of Technology, Hefei
    Ministry of Education, Hefei
    Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei)

  • Chao Fu

    (Hefei University of Technology, Hefei
    Ministry of Education, Hefei
    Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei)

  • Shan-Lin Yang

    (Hefei University of Technology, Hefei
    Ministry of Education, Hefei
    Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei)

Abstract

A group consensus reaching process (CRP) based on dynamic expert reliability is proposed in this paper. The method is designed to support uncertain multi-attribute group decision making in situations where experts in a group use distributed preference relations (DPRs) to express their preferences when making a decision. In the method, it is assumed that a predefined consensus requirement can be specified and must be satisfied before consensus-based solutions are generated. Consensus measures of DPRs are constructed to ensure consensus convergence and used to check whether the predefined consensus requirement at a specific level is satisfied. If the requirement is not satisfied, expert reliability is first defined and calculated in terms of data depicted by the experts, and then used to design an expert reliability based feedback mechanism composed of identification and suggestion rules to help identify the DPRs hindering CRP. Additionally, experts update their DPRs to accelerate convergence to CRP. Arguably, it is the first attempt to introduce expert reliability in consensus convergence. Once the predefined consensus requirement is satisfied, experts’ preferences are aggregated to generate a consensus-based solution. The problem of selecting an appropriate supplier in a high-end equipment manufacturing enterprise located in Changzhou, Jiangsu Province, China is analyzed by the proposed method to demonstrate its applicability and validity.

Suggested Citation

  • Min Xue & Chao Fu & Shan-Lin Yang, 2021. "Dynamic Expert Reliability Based Feedback Mechanism in Consensus Reaching Process with Distributed Preference Relations," Group Decision and Negotiation, Springer, vol. 30(2), pages 341-375, April.
    • Handle: RePEc:spr:grdene:v:30:y:2021:i:2:d:10.1007_s10726-020-09660-8
    DOI: 10.1007/s10726-020-09660-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10726-020-09660-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10726-020-09660-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dong, Qingxing & Cooper, Orrin, 2016. "A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making," European Journal of Operational Research, Elsevier, vol. 250(2), pages 521-530.
    2. 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.
    3. Fu, Chao & Yang, Jian-Bo & Yang, Shan-Lin, 2015. "A group evidential reasoning approach based on expert reliability," European Journal of Operational Research, Elsevier, vol. 246(3), pages 886-893.
    4. Gong, Zaiwu & Xu, Xiaoxia & Zhang, Huanhuan & Aytun Ozturk, U. & Herrera-Viedma, Enrique & Xu, Chao, 2015. "The consensus models with interval preference opinions and their economic interpretation," Omega, Elsevier, vol. 55(C), pages 81-90.
    5. Sidong Xian & Yafen Dong & Yubo Yin, 2017. "Interval-valued intuitionistic fuzzy combined weighted averaging operator for group decision making," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 895-905, August.
    6. Jiuping Xu & Zhibin Wu & Yuan Zhang, 2014. "A Consensus Based Method for Multi-criteria Group Decision Making Under Uncertain Linguistic Setting," Group Decision and Negotiation, Springer, vol. 23(1), pages 127-148, January.
    7. 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.
    8. Fischer, Gregory W., 1995. "Range Sensitivity of Attribute Weights in Multiattribute Value Models," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(3), pages 252-266, June.
    9. Bowen Zhang & Yucheng Dong & Enrique Herrera-Viedma, 2019. "Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching," Group Decision and Negotiation, Springer, vol. 28(3), pages 585-617, June.
    10. Ma, Li-Ching, 2016. "A new group ranking approach for ordinal preferences based on group maximum consensus sequences," European Journal of Operational Research, Elsevier, vol. 251(1), pages 171-181.
    11. Chao Fu & Dong-Ling Xu & Shan-Lin Yang, 2016. "Distributed preference relations for multiple attribute decision analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(3), pages 457-473, March.
    12. Mingwei Lin & Zeshui Xu & Yuling Zhai & Zhiqiang Yao, 2018. "Multi-attribute group decision-making under probabilistic uncertain linguistic environment," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(2), pages 157-170, February.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chenggang Wang, 2022. "Green Technology Innovation, Energy Consumption Structure and Sustainable Improvement of Enterprise Performance," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    2. Du, Junliang & Liu, Sifeng & Liu, Yong, 2022. "A limited cost consensus approach with fairness concern and its application," European Journal of Operational Research, Elsevier, vol. 298(1), pages 261-275.
    3. Peng Wu & Jinpei Liu & Ligang Zhou & Huayou Chen, 2022. "An Integrated Group Decision-Making Method with Hesitant Qualitative Information Based on DEA Cross-Efficiency and Priority Aggregation for Evaluating Factors Affecting a Resilient City," Group Decision and Negotiation, Springer, vol. 31(2), pages 293-316, April.

    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. Xue, Min & Fu, Chao & Yang, Shan-Lin, 2020. "Group consensus reaching based on a combination of expert weight and expert reliability," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Wenjun Chang & Chao Fu & Nanping Feng & Shanlin Yang, 2021. "Multi-criteria Group Decision Making with Various Ordinal Assessments," Group Decision and Negotiation, Springer, vol. 30(6), pages 1285-1314, December.
    3. Zhang, Bowen & Dong, Yucheng & Zhang, Hengjie & Pedrycz, Witold, 2020. "Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory," European Journal of Operational Research, Elsevier, vol. 287(2), pages 546-559.
    4. Gong, Zaiwu & Zhang, Huanhuan & Forrest, Jeffrey & Li, Lianshui & Xu, Xiaoxia, 2015. "Two consensus models based on the minimum cost and maximum return regarding either all individuals or one individual," European Journal of Operational Research, Elsevier, vol. 240(1), pages 183-192.
    5. 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.
    6. 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).
    7. 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.
    8. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    9. Fu, Chao & Chang, Wenjun & Xue, Min & Yang, Shanlin, 2019. "Multiple criteria group decision making with belief distributions and distributed preference relations," European Journal of Operational Research, Elsevier, vol. 273(2), pages 623-633.
    10. 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.
    11. Gong, Zaiwu & Guo, Weiwei & Herrera-Viedma, Enrique & Gong, Zejun & Wei, Guo, 2020. "Consistency and consensus modeling of linear uncertain preference relations," European Journal of Operational Research, Elsevier, vol. 283(1), pages 290-307.
    12. Yin Liu & Wenjun Chang & Xuefei Jia, 2023. "A Group Consensus Model for Multiple Attributes Group Decision Making with Interval Belief Distribution and Interval Distributed Preference Relation," Group Decision and Negotiation, Springer, vol. 32(3), pages 701-727, June.
    13. Fu, Chao & Yang, Jian-Bo & Yang, Shan-Lin, 2015. "A group evidential reasoning approach based on expert reliability," European Journal of Operational Research, Elsevier, vol. 246(3), pages 886-893.
    14. Du, Junliang & Liu, Sifeng & Liu, Yong, 2022. "A limited cost consensus approach with fairness concern and its application," European Journal of Operational Research, Elsevier, vol. 298(1), pages 261-275.
    15. Dong, Qingxing & Cooper, Orrin, 2016. "A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making," European Journal of Operational Research, Elsevier, vol. 250(2), pages 521-530.
    16. Tang, Ming & Liao, Huchang, 2021. "From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey," Omega, Elsevier, vol. 100(C).
    17. 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.
    18. Fernandez, Eduardo & Olmedo, Rafael, 2013. "An outranking-based general approach to solving group multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 225(3), pages 497-506.
    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. Sha Fan & Hengjie Zhang & Huali Tang, 2019. "A Linguistic Hierarchy Model with Self-Confidence Preference Relations and Its Application in Co-Regulation of Food Safety in China," IJERPH, MDPI, vol. 16(16), pages 1-21, August.

    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:spr:grdene:v:30:y:2021:i:2:d:10.1007_s10726-020-09660-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.