The inclusion-based LINMAP method for multiple criteria decision analysis within an interval-valued Atanassov's intuitionistic fuzzy environment

Ting Yu Chen*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

16 Scopus citations

Abstract

The purpose of this paper is to develop an inclusion-based LINMAP (i.e., Linear Programming Technique for Multidimensional Analysis of Preference) method for multiple criteria decision analysis that is based on interval-valued Atanassov's intuitionistic fuzzy sets. Using the inclusion comparison possibility in the interval-valued Atanassov's intuitionistic fuzzy context, an inclusion-based index of interval-valued Atanassov's intuitionistic fuzzy numbers is proposed that considers positive and negative ideals. An inclusion-based consistency index and an inclusion-based inconsistency index to measure the concordance and discordance, respectively, between paired comparison judgments are suggested. An inclusion-based LINMAP model is constructed using a linear programming technique to determine the optimal criterion weights and obtain the corresponding comprehensive inclusion-based index for each alternative. Then, the priority order of the alternatives can be acquired according to the comprehensive inclusion-based indices. The feasibility of the proposed method is illustrated using a practical problem that relates to the selection of bridge construction methods. A comparative analysis of other relevant decision-making methods is conducted to validate the effectiveness of the developed methodology.

Original languageEnglish
Pages (from-to)1325-1360
Number of pages36
JournalInternational Journal of Information Technology and Decision Making
Volume13
Issue number6
DOIs
StatePublished - 16 11 2014

Bibliographical note

Publisher Copyright:
© 2014 World Scientific Publishing Company.

Keywords

  • LINMAP
  • inclusion comparison possibility
  • inclusion-based LINMAP model
  • interval-valued Atanassov's intuitionistic fuzzy set
  • multiple criteria decision analysis

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