IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v67y2016i3p457-473.html
   My bibliography  Save this article

Distributed preference relations for multiple attribute decision analysis

Author

Listed:
  • Chao Fu

    (Hefei University of Technology, Anhui, P.R. China
    Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, P.R. China)

  • Dong-Ling Xu

    (The University of Manchester, Manchester, UK)

  • Shan-Lin Yang

    (Hefei University of Technology, Anhui, P.R. China
    Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, P.R. China)

Abstract

In this paper, we propose a new pairwise comparison approach called distributed preference relation (DPR) to simultaneously signify preferred, non-preferred, indifferent, and uncertain degrees of one alternative over another on a set of grades, which is more versatile for elicitation of preference information from a decision maker than multiplicative preference relation, fuzzy preference relation (FPR) and intuitionistic FPR. In a DPR matrix on a set of alternatives, each element is a distribution recording the preferred, non-preferred, indifferent, and uncertain degrees of one alternative over another using a set of grades. To facilitate the comparison of alternatives, we define a score matrix based on a DPR matrix using the given score values of the grades. Its additive consistency is constructed, analysed, and compared with the additive consistency of FPRs between alternatives. A method for comparing two interval numbers is then employed to create a possibility matrix from the score matrix, which can generate a ranking order of alternatives with possibility degrees. A problem of evaluating strategic emerging industries is investigated using the approach to demonstrate the application of a DPR matrix to modelling and analysing a multiple attribute decision analysis problem.

Suggested Citation

  • Chao Fu & Dong-Ling Xu & Shan-Lin Yang, 2016. "Distributed preference relations for multiple attribute decision analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(3), pages 457-473, March.
  • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:3:p:457-473
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/jors/journal/v67/n3/pdf/jors201571a.pdf
    File Function: Link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: http://www.palgrave-journals.com/jors/journal/v67/n3/full/jors201571a.html
    File Function: Link to full text HTML
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Chao Fu & Min Xue & Wenjun Chang, 2022. "Multiple criteria decision making with reliability of assessment," Annals of Operations Research, Springer, vol. 312(1), pages 121-157, May.
    2. Yin Liu & Wenjun Chang & Xuefei Jia, 2023. "A Group Consensus Model for Multiple Attributes Group Decision Making with Interval Belief Distribution and Interval Distributed Preference Relation," Group Decision and Negotiation, Springer, vol. 32(3), pages 701-727, June.
    3. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    4. Fu, Chao & Chang, Wenjun & Xue, Min & Yang, Shanlin, 2019. "Multiple criteria group decision making with belief distributions and distributed preference relations," European Journal of Operational Research, Elsevier, vol. 273(2), pages 623-633.
    5. Tim Chen & Hendri Daleanu & Chi-Huey Wong* & J.C.-Y. Chen, 2019. "Mathematical Derives of Evolutionary Algorithms for Multiple Criteria Decision Making," Sumerianz Journal of Scientific Research, Sumerianz Publication, vol. 2(1), pages 5-11, 01-2019.
    6. Min Xue & Chao Fu & Shanlin Yang, 2022. "A comparative analysis of probabilistic linguistic preference relations and distributed preference relations for decision making," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 71-97, March.
    7. Min Xue & Chao Fu & Shan-Lin Yang, 2021. "Dynamic Expert Reliability Based Feedback Mechanism in Consensus Reaching Process with Distributed Preference Relations," Group Decision and Negotiation, Springer, vol. 30(2), pages 341-375, April.

    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:pal:jorsoc:v:67:y:2016:i:3:p:457-473. 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.palgrave-journals.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.