Multi-Criteria Decision-Making with Linguistic Labels

Abstract

This paper proposes an approach that is suitable for solving multi-criteria decision-making problems characterized by fuzzy (subjective) criteria. A finite set (universe) of alternatives will be expressed as a decision table that represents a fuzzy information system in which every fuzzy criterion is connected with a set of its linguistic values. We apply subjective preference degrees for linguistic values that should be provided by a decision-maker. To simplify the process of decision-making in big data environments, an additional stage will be introduced that can produce a smaller set of alternatives represented by fuzzy linguistic labels of similarity classes. We select a small set of similarity classes for the final ranking. A measure of compatibility will be defined that should express the accordance of a selected alternative with preferences given for the linguistic values of a particular fuzzy criterion.