A fuzzy-monte carlo simulation approach for fault tree analysis
RAMS '06 Proceedings of the RAMS '06. Annual Reliability and Maintainability Symposium, 2006.
Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection
Expert Systems with Applications: An International Journal
A fuzzy extension of Saaty's priority theory
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Calibrated fuzzy AHP for current bank account selection
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.