Extreme cases that contain either extremely high or pretty low preference attribute(s) are investigated for multi-attribute decision making problems. Normal cases occur most of the time, and many existing methods have been developed to support the decision making in such scenarios. Extreme cases are possible in real applications, and they are usually present intriguing scenarios because of the potential fuzzy and varying decision criteria. To capture this phenomenon, varying weights are introduced to simulate the change pattern concerning relative importance of attributes, and a uniform framework has been developed to support the decision making mathematically for extreme cases. A real application from Industrial Assessment Center at Oregon State University is used to demonstrate the proposed method, and the result shows its capability of capturing a decision maker’s flexible decision altitudes, and indicates its advantage over existing constant weight methods.

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