IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/463930.html
   My bibliography  Save this article

Objective Attributes Weights Determining Based on Shannon Information Entropy in Hesitant Fuzzy Multiple Attribute Decision Making

Author

Listed:
  • Yingjun Zhang
  • Yizhi Wang
  • Jingping Wang

Abstract

Hesitant fuzzy set has been an important tool in dealing with multiple attribute decision making (MADM) problems, especially for the decision making situation when only some values of membership are possible for an alternative on attributes. However, determining attributes weights in hesitant fuzzy MADM is still an open problem. In this paper, we propose an objective weighting approach based on Shannon information entropy, which expresses the relative intensities of attribute importance to signify the average intrinsic information transmitted to the decision maker. Furthermore, we construct a hesitant fuzzy MADM approach based on the TOPSIS method and a weighted correlation coefficient proposed in this paper. Finally, we utilize a supplier selection example to validate the objective attributes weights determining method and the proposed hesitant fuzzy MADM approach.

Suggested Citation

  • Yingjun Zhang & Yizhi Wang & Jingping Wang, 2014. "Objective Attributes Weights Determining Based on Shannon Information Entropy in Hesitant Fuzzy Multiple Attribute Decision Making," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, April.
    • Handle: RePEc:hin:jnlmpe:463930
    DOI: 10.1155/2014/463930
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2014/463930.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2014/463930.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2014/463930?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
    ---><---

    Citations

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


    Cited by:

    1. Wu, Hui & Li, Xiaojuan & Lu, Hongna & Tong, Ling & Kang, Shaozhong, 2023. "Crop acreage planning for economy- resource- efficiency coordination: Grey information entropy based uncertain model," Agricultural Water Management, Elsevier, vol. 289(C).
    2. Iwona Cieślak & Andrzej Biłozor, 2021. "An Analysis of an Area’s Vulnerability to the Emergence of Land-Use Conflicts," Land, MDPI, vol. 10(11), pages 1-18, November.

    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:hin:jnlmpe:463930. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.