IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-16-0382-2_12.html
   My bibliography  Save this book chapter

GSSOA Using Double Hierarchy Hesitant Linguistic Sets and Decision Field Theory

In: Green Supplier Evaluation and Selection: Models, Methods and Applications

Author

Listed:
  • Hu-Chen Liu

    (Tongji University)

  • Xiao-Yue You

    (Tongji University)

Abstract

With the increasing pressure from global competition, manufacturers have realized that green production is significant in supply chain management. Green supplier selection and order allocation (GSSOA) play a distinct and critical role for organizations to achieve green development and build competitive advantage. In this chapter, we develop a GSSOA model for selecting the most suitable green suppliers and determining the optimal order sizes among them. First, double hierarchy hesitant linguistic term sets (DHHLTSs) are adopted to deal with uncertainty in evaluating the green performance of alternative suppliers. Then, an extended decision field theory is proposed to choose efficient green suppliers dynamically. Considering quantity discount, a multi-objective linear programming model is established to allocate reasonable order quantities among the selected suppliers. The applicability and effectiveness of the developed model are illustrated through its application in the electronic industry and a comparative analysis with other methods.

Suggested Citation

  • Hu-Chen Liu & Xiao-Yue You, 2021. "GSSOA Using Double Hierarchy Hesitant Linguistic Sets and Decision Field Theory," Springer Books, in: Green Supplier Evaluation and Selection: Models, Methods and Applications, chapter 0, pages 273-295, Springer.
  • Handle: RePEc:spr:sprchp:978-981-16-0382-2_12
    DOI: 10.1007/978-981-16-0382-2_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:sprchp:978-981-16-0382-2_12. 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.