4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      A Linear Programming Method Based on an Improved Score Function for Interval-Valued Pythagorean Fuzzy Numbers and Its Application to Decision-Making

      1
      International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
      World Scientific Pub Co Pte Lt

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The present paper proposes an improved score function for solving multi-criteria decision-making (MCDM) problem with partially known weight information, In it, the preferences related to criteria are taken in the form of interval-valued Pythagorean fuzzy sets. Based on these preferences and an improved score function, a score matrix has been formulated and then a linear programming based method has been proposed to solve MCDM problems with unknown attribute weights. Some generalized properties have also been proven for justification. Illustrative examples have been given for showing the superiority of the approach with the other existing functions in the decision-making process.

          Related collections

          Author and article information

          Contributors
          Journal
          International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
          Int. J. Unc. Fuzz. Knowl. Based Syst.
          World Scientific Pub Co Pte Lt
          0218-4885
          1793-6411
          January 31 2018
          February 2018
          January 31 2018
          February 2018
          : 26
          : 01
          : 67-80
          Affiliations
          [1 ]School of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala 147004, Punjab, India
          Article
          10.1142/S0218488518500046
          17318373-bc9a-4fe1-832c-6f502c0c8f7c
          © 2018
          History

          Comments

          Comment on this article