Ranking Alternatives based on Imprecise Multi-Criteria Data and Pairwise Overlap Dominance Relations

This paper explores a multi-criteria outranking methodology that is designed to both handle uncertain and imprecise data in describing alternatives as well as treating the decision maker's preference information in a sensible way that re flects the difficulties in articulating preferences. Based on fuzzy interval degrees, representing and measuring data imprecision, this procedure obtains a set of semi-equivalence classes assigning an intransitive order on the alternatives. Relevance measures are then explored for ranking alternatives with respect to the semi-equivalence classes, and a final illustrative example is given for comparison with standard methods like PROMETHEE. The proposed methodology takes into account the risk attitudes of decision makers, organizing the alternatives and ranking them according to their relevance. The whole interactive decision support allows understanding the dependencies among the alternatives and how they can be resolved if a finer ranking is preferred.