A new linguistic MCDM method based on multiple-criterion data fusion

Yong Deng, Tung Sun Chan, Ying Wu, Dong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

126 Citations (Scopus)

Abstract

Multiple-criteria decision-making (MCDM) is concerned with the ranking of decision alternatives based on preference judgements made on decision alternatives over a number of criteria. First, taking advantage of data fusion technology to comprehensively consider each criterion data is a reasonable idea to solve the MCDM problem. Second, in order to efficiently handle uncertain information in the process of decision making, some well developed mathematical tools, such as fuzzy sets theory and Dempster Shafer theory of evidence, are used to deal with MCDM. Based on the two main reasons above, a new fuzzy evidential MCDM method under uncertain environments is proposed. The rating of the criteria and the importance weight of the criteria are given by experts' judgments, represented by triangular fuzzy numbers. Then, the weights are transformed into discounting coefficients and the ratings are transformed into basic probability assignments. The final results can be obtained through the Dempster rule of combination in a simple and straight way. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.
Original languageEnglish
Pages (from-to)6985-6993
Number of pages9
JournalExpert Systems with Applications
Volume38
Issue number6
DOIs
Publication statusPublished - 1 Jun 2011

Keywords

  • Dempster-Shafer evidence theory
  • Fuzzy sets theory
  • MCDM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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