Uncertain Preference Assessment using Familiar Alternatives to Decision Maker

In Analytic Hierarchy Process (AHP), a decision problem is structured hierarchically with the criteria and alternatives. The propriety weight vector of the alternatives is obtained as the sums of their local weights considering the importance of the criteria. Once a decision maker finds out his/her preference as the importance of criteria, s/he can evaluate the other sets of alternatives. The preference for criteria is essential and independent of the alternatives. However, the criteria are intangible so that it sometimes difficult for a decision maker to give judgments on them directly. Hence, we propose a method to help a decision maker to find out his/her preference based on his/her experience. It is much easier for him/her to give the judgments on the alternatives that s/he knows well. The preference is denoted as a fuzzy vector of criteria from a possibilistic view. We also propose the model to obtain an interval score vector of new alternatives from the derived preference and their given local scores. Both models of deriving a preference vector and a score vector are based on interval inclusion relation.