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A new method for deriving priority weights by extracting consistent numerical-valued matrices from interval-valued fuzzy judgement matrix

Zhang,F, Ignatius,J, Lim,CP and Zhao,Y 2014, A new method for deriving priority weights by extracting consistent numerical-valued matrices from interval-valued fuzzy judgement matrix, Information Sciences, vol. 279, pp. 280-300, doi: 10.1016/j.ins.2014.03.120.

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Title A new method for deriving priority weights by extracting consistent numerical-valued matrices from interval-valued fuzzy judgement matrix
Author(s) Zhang,F
Ignatius,J
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Zhao,Y
Journal name Information Sciences
Volume number 279
Start page 280
End page 300
Total pages 21
Publisher Elsevier
Place of publication Philadelphia, United States
Publication date 2014-09-20
ISSN 0020-0255
Keyword(s) Consistency
Fuzzy judgment matrix
Pairwise comparison matrix
Priority weight vector
Summary It is important to derive priority weights from interval-valued fuzzy preferences when a pairwise comparative mechanism is used. By focusing on the significance of consistency in the pairwise comparison matrix, two numerical-valued consistent comparison matrices are extracted from an interval fuzzy judgement matrix. Both consistent matrices are derived by solving the linear or nonlinear programming models with the aid of assessments from Decision Makers (DMs). An interval priority weight vector from the extracted consistent matrices is generated. In order to retain more information hidden in the intervals, a new probability-based method for comparison of the interval priority weights is introduced. An algorithm for deriving the final priority interval weights for both consistent and inconsistent interval matrices is proposed. The algorithm is also generalized to handle the pairwise comparison matrix with fuzzy numbers. The comparative results from the five examples reveal that the proposed method, as compared with eight existing methods, exhibits a smaller degree of uncertainty pertaining to the priority weights, and is also more reliable based on the similarity measure. © 2014 Elsevier Inc. All rights reserved.
Language eng
DOI 10.1016/j.ins.2014.03.120
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069981

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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