A knowledge-based outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets

The modeling and solving of multi-criteria decision-making (MCDM) problems under uncertainty is still a challenging topic. In real-life decision-making, using linguistic terms to represent experts' judgments is suitable and straightforward since precise quantitative values may often be unavailable or the cost for their computation is too high. The introduction of hesitant fuzzy linguistic term sets (HFLTSs) was motivated by the limitations of prior linguistic fuzzy models and need for richer linguistic tools. However, since their introduction, comparing HFLTSs is still one of the major concerns of researchers in this area. The existing approaches in the literature commonly rely on (1) labels and intervals from the linguistic terms as the central elements of an envelope-based approach or (2) linguistic scale functions as the basis of a distance-based approach. The two approaches retain certain shortcomings resulting information distortion and loss which may inevitably degrade their credibility. In this paper, the authors are involved in the recent proposal of combining outranking approaches with HFLTSs in an MCDM context. After reviewing the existing approaches, an outranking method based on a novel knowledge-based paradigm for comparing HFLTSs is developed. Alternatively, the paradigm's foundations are the introduced concepts of fuzzy preference relations and profiles considering uncertainty degrees in decision makers' assessments. The paradigm is then associated with a multi-criteria relational clustering (MCRC) algorithm that additionally extracts fuzzy preference relations between the resultant clusters. Last, an illustrative example is given to verify the appropriateness and efficacy of the developed approach and comparisons are made with other existing ones. (C) 2017 Elsevier B.V. All rights reserved.