IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v23y2023i4d10.1007_s12351-023-00804-7.html
   My bibliography  Save this article

Group game cross-efficiency order allocation model based on regret theory and decision consensus

Author

Listed:
  • Xing Shao

    (Guizhou University)

  • Meiqiang Wang

    (Guizhou University)

Abstract

The traditional order allocation model primarily adopts a multi-objective programming approach. This approach is optimized for profit maximization and cost minimization to obtain the order allocation scheme. However, supplier efficiency is typically an important overlooked index in the order allocation process. In addition, only one decision-maker (DM) usually participates in the decision-making process. Furthermore, the bounded rationality of DMs under the risk state is not considered. Therefore, we established a group game cross-efficiency order allocation model (GGCE-OA) based on regret theory and decision consensus. First, regret theory was used to describe DMs’ subjective preferences and capture their bounded rational behaviours. Second, based on the contribution degree and the corresponding iterative algorithm, the weight modification model is established under the group game cross-efficiency evaluation framework to improve the decision consensus among DMs. Third, to allocate orders from the perspective of improving supplier efficiency, we established the GGCE-OA model based on regret theory and decision consensus, as well as establishing the corresponding iterative algorithm. This enabled us to obtain a fair, unique and accepted order allocation scheme by all DMs. Finally, the model established in this paper was applied for order allocations to green suppliers to verify its effectiveness and practicability.

Suggested Citation

  • Xing Shao & Meiqiang Wang, 2023. "Group game cross-efficiency order allocation model based on regret theory and decision consensus," Operational Research, Springer, vol. 23(4), pages 1-39, December.
  • Handle: RePEc:spr:operea:v:23:y:2023:i:4:d:10.1007_s12351-023-00804-7
    DOI: 10.1007/s12351-023-00804-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-023-00804-7
    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/s12351-023-00804-7?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.

    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:operea:v:23:y:2023:i:4:d:10.1007_s12351-023-00804-7. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.