IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v59y2022i3d10.1007_s12597-022-00571-7.html
   My bibliography  Save this article

A multivariate minimum cost consensus approach for two-level group decision making

Author

Listed:
  • Yong Liu

    (Jiangnan University)

  • Ting Zhou

    (Jiangnan University
    Yantai Nanshan University)

  • Wei-xue Diao

    (Jiangnan University)

  • Jinhong Yi

    (Jiangnan University)

Abstract

There exist a variety of two-level group decision making problems with multiple constraints. Two-level group decision-making refers to decisions made when there is a decision-making relationship between the upper and lower levels in the decision-making environment. For example, the issue of Sino-US trade decision-making: Sino-US trade decision-making is not only affected by factors in the international environment and national relations, but also restricted by different domestic stakeholders. Therefore, the study of Sino-US trade decisions from a single level (international level or domestic level) has a certain degree of one-sidedness. China-US trade decision-making is a process of decision-making in the national interests of China and the United States. At the international level, national interest is a holistic concept, while at the domestic level, national interest is the integration of domestic interests. Therefore, Sino-US trade decision-making is a process where the two countries' national interests and domestic interests work together. Therefore, the importance of studying the two-level group decision-making problem is becoming more and more prominent. To reach a scientific consensus and reduce consumes of time, labor and funds as a result of multiple rounds of negotiation between interest groups at different level, by introducing two-level decision and multivariate programming into the minimum cost consensus model, we establish a multivariate minimum cost consensus model for two-level decision making. First, we construct a multivariable minimum cost consensus model of the intra-group that considers subordinate DMs. Second, considering the achievement of inter-group consensus and superior DMs, from the perspective of group negotiation and system coordination, we use the asymmetric Nash bargaining theory to construct an inter-group consensus model, and then we exploit the proposed approach to solve the global consensus of the intra-group and inter-group levels. Finally, the proposed approach is exploited to deal with the problem of pollutant incineration from waste incineration.

Suggested Citation

  • Yong Liu & Ting Zhou & Wei-xue Diao & Jinhong Yi, 2022. "A multivariate minimum cost consensus approach for two-level group decision making," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 839-861, September.
    • Handle: RePEc:spr:opsear:v:59:y:2022:i:3:d:10.1007_s12597-022-00571-7
    DOI: 10.1007/s12597-022-00571-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-022-00571-7
    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/s12597-022-00571-7?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. Liu, Bingsheng & Shen, Yinghua & Zhang, Wei & Chen, Xiaohong & Wang, Xueqing, 2015. "An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making," European Journal of Operational Research, Elsevier, vol. 245(1), pages 209-225.
    2. 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.
    3. Wu, Zhibin & Xu, Jiuping, 2016. "Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations," Omega, Elsevier, vol. 65(C), pages 28-40.
    4. 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.
    5. Fanyong Meng & Qingxian An & Xiaohong Chen, 2016. "A consistency and consensus-based method to group decision making with interval linguistic preference relations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(11), pages 1419-1437, November.
    6. Yejun Xu & Dou Rui & Huimin Wang, 2017. "A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(6), pages 1311-1321, April.
    7. Chaudhuri, Ananish & Paichayontvijit, Tirnud & So, Tony, 2015. "Team versus individual behavior in the minimum effort coordination game," Journal of Economic Psychology, Elsevier, vol. 47(C), pages 85-102.
    8. R. O. Parreiras & P. Ya. Ekel & D. C. Morais, 2012. "Fuzzy Set Based Consensus Schemes for Multicriteria Group Decision making Applied to Strategic Planning," Group Decision and Negotiation, Springer, vol. 21(2), pages 153-183, March.
    9. Wang, Zhou-Jing & Li, Kevin W., 2015. "A multi-step goal programming approach for group decision making with incomplete interval additive reciprocal comparison matrices," European Journal of Operational Research, Elsevier, vol. 242(3), pages 890-900.
    10. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    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. Yong Liu & Ting Zhou & Jeffrey Yi-Lin Forrest, 2020. "A Multivariate Minimum Cost Consensus Model for Negotiations of Holdout Demolition," Group Decision and Negotiation, Springer, vol. 29(5), pages 871-899, October.
    2. 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.
    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. 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).
    5. 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.
    6. 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.
    7. 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.
    8. Wenqi Liu & Hengjie Zhang & Haiming Liang & Cong-cong Li & Yucheng Dong, 2022. "Managing Consistency and Consensus Issues in Group Decision-Making with Self-Confident Additive Preference Relations and Without Feedback: A Nonlinear Optimization Method," Group Decision and Negotiation, Springer, vol. 31(1), pages 213-240, February.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. Liu, Bingsheng & Zhou, Qi & Ding, Ru-Xi & Palomares, Iván & Herrera, Francisco, 2019. "Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination," European Journal of Operational Research, Elsevier, vol. 275(2), pages 737-754.
    14. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.
    15. Tang, Ming & Liao, Huchang & Xu, Jiuping & Streimikiene, Dalia & Zheng, Xiaosong, 2020. "Adaptive consensus reaching process with hybrid strategies for large-scale group decision making," European Journal of Operational Research, Elsevier, vol. 282(3), pages 957-971.
    16. Meng, Fanyong & Tan, Chunqiao & Chen, Xiaohong, 2017. "Multiplicative consistency analysis for interval fuzzy preference relations: A comparative study," Omega, Elsevier, vol. 68(C), pages 17-38.
    17. Pei Wang & Xuanhua Xu & Shuai Huang, 2019. "An Improved Consensus-Based Model for Large Group Decision Making Problems Considering Experts with Linguistic Weighted Information," Group Decision and Negotiation, Springer, vol. 28(3), pages 619-640, June.
    18. 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.
    19. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    20. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.

    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:opsear:v:59:y:2022:i:3:d:10.1007_s12597-022-00571-7. 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.