Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13742
Title: A new linguistic MCDM method based on multiple-criterion data fusion
Authors: Deng, Y
Chan, FTS 
Wu, Y
Wang, D
Keywords: Dempster-Shafer evidence theory
Fuzzy sets theory
MCDM
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 6, p. 6985-6993 How to cite?
Journal: Expert systems with applications 
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.
URI: http://hdl.handle.net/10397/13742
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2010.12.016
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