Multi-attribute Decision-making based on Interval-valued Intuitionistic Fuzzy Sets

Juan Liu, Ya Li, Xinyang Deng, Daijun Wei, Yong Deng

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Multi-attribute Decision-making (MADM) plays an important role in many applications such as risk analysis, expert systems and military systems. Due to the efficiency to handle uncertain information, Interval-valued Intuitionistic Fuzzy Sets (IVIFS) is widely used to model vague information. In this paper, a new MADM method based on the power average operator is proposed to efficiently combine different attributes data modeled as IVIFS. A new score function is developed in the proposed method. Compared with previous methods, the proposed method is more efficient due to the fact that the difference of span of each interval number is taken into consideration. A numerical example is presented to demonstrate the application and efficiency of the proposed method.

Original languageEnglish
Pages (from-to)1107-1114
Number of pages8
JournalJournal of Information and Computational Science
Volume9
Issue number4
StatePublished - Apr 2012
Externally publishedYes

Keywords

  • Interval-valued Intuitionistic Fuzzy Sets
  • Multi-attribute decision-making
  • Power average operator
  • Score function
  • Similarity degree

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