Multi-attribute decision-making method based on interval-valued intuitionistic fuzzy sets and D-S theory of evidence

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

The theory of interval-valued intuitionistic fussy sets is now widely studied to deal with vagueness and D-S theory of evidence has a widespread use in multi-attribute decision-making (MADM) problems under uncertain situation. In this paper, A new method based on interval-valued intuitionistic fuzzy sets and D-S theory of evidence is proposed to handle MADM problems. In our method, the interval-valued intuitionistic fuzzy numbers are represented by the interval average numbers. These average numbers are assigned to corresponding basic probability assignment (bpa) based on discounting method. Then the D-S combination rule is used to fuse information in order to obtain final mass functions for each alternative, thus the order of each alternative is obtained. A numerical example is used to illustrate the efficiency of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012
Pages2651-2654
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 24th Chinese Control and Decision Conference, CCDC 2012 - Taiyuan, China
Duration: 23 May 201225 May 2012

Publication series

NameProceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012

Conference

Conference2012 24th Chinese Control and Decision Conference, CCDC 2012
Country/TerritoryChina
CityTaiyuan
Period23/05/1225/05/12

Keywords

  • D-S theory of evidence
  • Data fusion
  • Interval average number
  • Interval-valued intuitionistic fuzzy sets
  • Multi-attribute decision-making

Fingerprint

Dive into the research topics of 'Multi-attribute decision-making method based on interval-valued intuitionistic fuzzy sets and D-S theory of evidence'. Together they form a unique fingerprint.

Cite this