IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v71y2020i5p831-845.html
   My bibliography  Save this article

Partitioned fuzzy measure-based linear assignment method for Pythagorean fuzzy multi-criteria decision-making with a new likelihood

Author

Listed:
  • Decui Liang
  • Adjei Peter Darko
  • Zeshui Xu
  • Yinrunjie Zhang

Abstract

The aim of this paper is to develop an extended linear assignment method to solve multi-criteria decision-making (MCDM) problems under the Pythagorean fuzzy environment, where the criteria values take the form of the Pythagorean fuzzy numbers (PFNs) and the information about criteria weights are correlative. In order to obtain the criteria-wise rankings of the linear assignment method, we firstly define a new likelihood for the comparison between PFNs. Then, we introduce the fuzzy measure to determine the weighted-rank frequency matrix of the linear assignment method. Unlike the existing literature of the fuzzy measure, this paper incorporates the partitioned structure of the criteria set into it and proposes a new partitioned fuzzy measure. Further, we design the extended linear assignment method by using the new likelihood of PFNs and partitioned fuzzy measure for Pythagorean fuzzy multi-criteria decision-making (PFMCDM). Finally, a practical example is used to illustrate and verify our proposed method.

Suggested Citation

  • Decui Liang & Adjei Peter Darko & Zeshui Xu & Yinrunjie Zhang, 2020. "Partitioned fuzzy measure-based linear assignment method for Pythagorean fuzzy multi-criteria decision-making with a new likelihood," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(5), pages 831-845, May.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:5:p:831-845
    DOI: 10.1080/01605682.2019.1590133
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2019.1590133
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01605682.2019.1590133?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.

    Citations

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


    Cited by:

    1. Aniruddh Nain & Deepika Jain & Shivam Gupta & Ashwani Kumar, 2023. "Improving First Responders' Effectiveness in Post-Disaster Scenarios Through a Hybrid Framework for Damage Assessment and Prioritization," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 409-437, September.

    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:taf:tjorxx:v:71:y:2020:i:5:p:831-845. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    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.