IDEAS home Printed from https://ideas.repec.org/a/igg/joris0/v5y2014i3p27-49.html
   My bibliography  Save this article

Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets

Author

Listed:
  • John Robinson

    (P, PG & Research Department of Mathematics, Bishop Heber College, Tiruchirappalli, India)

  • Henry Amirtharaj

    (E.C., PG & Research Department of Mathematics, Bishop Heber College, Tiruchirappalli, India)

Abstract

A new approach for multiple attribute group decision making (MAGDM) problems where the attribute weights and the expert weights are real numbers and the attribute values take the form of vague values, is presented in this paper. Since families of ordered weighted averaging (OWA) operators are available in the literature, and only a few available for vague sets, the vague ordered weighted averaging (VOWA) operator and the induced vague ordered weighted averaging (IVOWA) operator are introduced in this paper and utilized for aggregating the vague information. The correlation coefficient for vague sets is used for ranking the alternatives and a new MAGDM model is developed based on the IVOWA operator and the vague weighted averaging (VWA) operator. In addition to the proposed model, two different models are proposed based on Linguistic Quantifiers for the situation when the expert weights are completely unknown. An illustrative example is given and a comparison is made between the models to demonstrate the applicability of the proposed approach of MAGDM.

Suggested Citation

  • John Robinson & Henry Amirtharaj, 2014. "Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 5(3), pages 27-49, July.
    • Handle: RePEc:igg:joris0:v:5:y:2014:i:3:p:27-49
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijoris.2014070102
    Download Restriction: no
    ---><---

    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:igg:joris0:v:5:y:2014:i:3:p:27-49. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.