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Regime switching fuzzy AHP model for choice-varying priorities problem and expert consistency prioritization: A cubic fuzzy-priority matrix design

[Display omitted] ►The RS-FAHP deals with the choice-varying priority (CVP) problem. ► The cumulative priority matrix is in the 3D form including every regime and priority matrices. ► The centric consistency index (CCI) is utilized for consistency control. ► The fuzzy Monte-Carlo simulation framewor...

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Bibliographic Details
Published in:Expert systems with applications 2012-04, Vol.39 (5), p.4954-4964
Main Authors: Duru, Okan, Bulut, Emrah, Yoshida, Shigeru
Format: Article
Language:English
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Summary:[Display omitted] ►The RS-FAHP deals with the choice-varying priority (CVP) problem. ► The cumulative priority matrix is in the 3D form including every regime and priority matrices. ► The centric consistency index (CCI) is utilized for consistency control. ► The fuzzy Monte-Carlo simulation framework is applied for the loss probability calculation. The aim of this paper is to develop a regime switching design of the fuzzy analytic hierarchy process (FAHP) and to improve its functionality under the choice-varying priority (CVP) problem. In the conventional AHP decision process, priority matrices are identical and their values are invariant for a specific objective. However, in many Multi-Criteria Decision Making (MCDM) problems, the relative importance of criteria may differ according to the choices. A regime switching process is proposed for improving the CVP problem. Under the fuzzy-AHP (FAHP) framework, choice-varying priorities are presented in a cubic matrix form. Another novel contribution is suggested in the prioritization of the level of expert consistency. During the decision-making practice, experts may have different attitudes and their individual matrix consistencies might be superior or inferior in their overall practices. Individual consistency is one of the objective indicators of the quality of judgment. An expert consistency prioritization approach is proposed to deal with the classification of response stability. For the financial risk assessment part of the study, the loss probability of the intended projects is calculated by the fuzzy Monte-Carlo simulation framework.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.10.020