A method based on multiobjective optimization for deriving a ranking from a fuzzy preference relation

Methods for deriving final ranking from a fuzzy preference relation do not perform well in presence of irrelevant alternatives or in case of complex graphs with numerous circuits. A new method for improving the quality of their prescriptions is proposed here; it rests on the idea of reducing differences between a global model of preferences and a final ranking via multiobjective optimization. This proposal is also more robust than others in relation to irrelevant alternatives. It can be seen as an intent of reconciling two schools of thought related to decision aid; one approach accepts the fuzziness of preferences; the other is based on a system of axioms that model a normative rationality.