IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v32y2023i6d10.1007_s10726-023-09845-x.html
   My bibliography  Save this article

Heterogeneous Multi-attribute Large-Scale Group Decision-Making Considering Individual Concerns and Information Credibility

Author

Listed:
  • Kaixin Gong

    (Tongji University)

  • Weimin Ma

    (Tongji University)

  • Hui Zhang

    (Tongji University)

  • Mark Goh

    (National University of Singapore)

Abstract

Multi-attribute Large-Scale Group Decision-Making (MALSGDM) problems require a plethora of Decision Makers (DMs) with different knowledge structures to evaluate the decision alternatives with respect to the multiple attributes of the problem. To deal with the heterogeneous assessment information provided by the DMs with different concerns, this study develops a heterogeneous MALSGDM method considering individual concerns and information credibility. Under heterogeneous attribute concerns, an approach for fusing individual preference information is presented utilizing Dempster–Shafer theory. A method for determining the weights of each subgroup is given by combining the subgroup size and the credibility of the subgroup preference information. Next, a hybrid consensus measure is proposed to compute the consensus level of the heterogeneous preference information. A feedback mechanism based on the unit adjustment cost is then designed to promote consensus reaching. Finally, an analysis and discussion are performed to validate the value of this research.

Suggested Citation

  • Kaixin Gong & Weimin Ma & Hui Zhang & Mark Goh, 2023. "Heterogeneous Multi-attribute Large-Scale Group Decision-Making Considering Individual Concerns and Information Credibility," Group Decision and Negotiation, Springer, vol. 32(6), pages 1315-1349, December.
    • Handle: RePEc:spr:grdene:v:32:y:2023:i:6:d:10.1007_s10726-023-09845-x
    DOI: 10.1007/s10726-023-09845-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10726-023-09845-x
    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/s10726-023-09845-x?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. Wade D. Cook & Lawrence M. Seiford, 1978. "Priority Ranking and Consensus Formation," Management Science, INFORMS, vol. 24(16), pages 1721-1732, December.
    2. Kou, Gang & Lin, Changsheng, 2014. "A cosine maximization method for the priority vector derivation in AHP," European Journal of Operational Research, Elsevier, vol. 235(1), pages 225-232.
    3. 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).
    4. 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.
    5. Tang, Ming & Liao, Huchang & Mi, Xiaomei & Lev, Benjamin & Pedrycz, Witold, 2021. "A hierarchical consensus reaching process for group decision making with noncooperative behaviors," European Journal of Operational Research, Elsevier, vol. 293(2), pages 632-642.
    6. Ljubisa Vlacic & Michio Amagasa & Akira Ishikawa & Theodore Williams & Giichi Tomizawa, 1997. "Applying Multiattribute-Based Group Decision Making Techniques in Complex Equipment Selection Tasks," Group Decision and Negotiation, Springer, vol. 6(6), pages 529-556, December.
    7. Jianjun Zhu & Shitao Zhang & Ye Chen & Lili Zhang, 2016. "A Hierarchical Clustering Approach Based on Three-Dimensional Gray Relational Analysis for Clustering a Large Group of Decision Makers with Double Information," Group Decision and Negotiation, Springer, vol. 25(2), pages 325-354, March.
    8. Li, Yanhong & Kou, Gang & Li, Guangxu & Peng, Yi, 2022. "Consensus reaching process in large-scale group decision making based on bounded confidence and social network," European Journal of Operational Research, Elsevier, vol. 303(2), pages 790-802.
    9. 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.
    10. 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.
    11. Weimin Ma & Kaixin Gong & Zhangpeng Tian, 2023. "Heterogeneous large-scale group decision making with subgroup leaders: An application to the green supplier selection," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(6), pages 1570-1586, June.
    12. Zeshui Xu, 2006. "A Note on Linguistic Hybrid Arithmetic Averaging Operator in Multiple Attribute Group Decision Making with Linguistic Information," Group Decision and Negotiation, Springer, vol. 15(6), pages 593-604, November.
    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. Dombi, József & Jónás, Tamás, 2024. "Consensus measures based on a fuzzy concept," European Journal of Operational Research, Elsevier, vol. 315(2), pages 642-653.
    2. Shen, Yufeng & Ma, Xueling & Kou, Gang & Rodríguez, Rosa M. & Zhan, Jianming, 2025. "Consensus methods with Nash and Kalai–Smorodinsky bargaining game for large-scale group decision-making," European Journal of Operational Research, Elsevier, vol. 321(3), pages 865-883.
    3. Zhou, Jian-Lan & Zhou, Ya-Lun & Xiao, Ren-Bin, 2025. "A large-scale group SLIM considering expert credibility under social network to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    4. Meng, Fan-Yong & Zhao, Deng-Yu & Gong, Zai-Wu & Chu, Jun-Fei & Pedrycz, Witold & Yuan, Zhe, 2024. "Consensus adjustment for multi-attribute group decision making based on cross-allocation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 200-216.
    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. Meng, Fanyong & Tang, Jie & An, Qingxian, 2023. "Cooperative game based two-stage consensus adjustment mechanism for large-scale group decision making," Omega, Elsevier, vol. 117(C).
    7. Guo, Weiwei & Gong, Zaiwu & Zhang, Wei-Guo & Xu, Yanxin, 2023. "Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1279-1295.
    8. Jinmuzi Zhang & Haiyan Xu & Ginger Y. Ke, 2024. "A Novel Consensus and Dissent Framework Under Grey Preference Based on the Graph Model for Conflict Resolution for Two Decision Makers," Group Decision and Negotiation, Springer, vol. 33(4), pages 711-744, August.
    9. Tang, Ming & Liao, Huchang, 2024. "Group efficiency and individual fairness tradeoff in making wise decisions," Omega, Elsevier, vol. 124(C).
    10. 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.
    11. Meng, Fan-Yong & Gong, Zai-Wu & Pedrycz, Witold & Chu, Jun-Fei, 2023. "Selfish-dilemma consensus analysis for group decision making in the perspective of cooperative game theory," European Journal of Operational Research, Elsevier, vol. 308(1), pages 290-305.
    12. Monica Martinez-Blasco & Vanessa Serrano & Francesc Prior & Jordi Cuadros, 2023. "Analysis of an event study using the Fama–French five-factor model: teaching approaches including spreadsheets and the R programming language," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    13. Jabeur, Khaled & Martel, Jean-Marc, 2007. "A collective choice method based on individual preferences relational systems (p.r.s.)," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1549-1565, March.
    14. Tang, Ming & Liao, Huchang, 2021. "Failure mode and effect analysis considering the fairness-oriented consensus of a large group with core-periphery structure," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Xunjie Gou & Zeshui Xu & Huchang Liao, 2019. "Hesitant Fuzzy Linguistic Possibility Degree-Based Linear Assignment Method for Multiple Criteria Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 35-63, January.
    16. Qian-Yun Tan & Cui-Ping Wei & Qi Liu & Xiang-Qian Feng, 2016. "The Hesitant Fuzzy Linguistic TOPSIS Method Based on Novel Information Measures," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(05), pages 1-22, October.
    17. Mei Cai & Yilong Heng, 2025. "Bidirectional Feedback Mechanism in Group Decision-Making: A Quantum Probability Theory Model Based on Interference Effects," Mathematics, MDPI, vol. 13(3), pages 1-24, January.
    18. Viral Gupta & P. K. Kapur & Deepak Kumar, 2019. "Prioritizing and Optimizing Disaster Recovery Solution using Analytic Network Process and Multi Attribute Utility Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 171-207, January.
    19. Mahmut Baydaş & Orhan Emre Elma & Željko Stević, 2024. "Proposal of an innovative MCDA evaluation methodology: knowledge discovery through rank reversal, standard deviation, and relationship with stock return," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    20. Andrea Aveni & Ludovico Crippa & Giulio Principi, 2024. "On the Weighted Top-Difference Distance: Axioms, Aggregation, and Approximation," Papers 2403.15198, arXiv.org, revised Mar 2024.

    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:32:y:2023:i:6:d:10.1007_s10726-023-09845-x. 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.