A new decision making model based on Rank Centrality for GDM with fuzzy preference relations

Preference aggregation in Group Decision Making (GDM) is a substantial problem that has received lot of research attention. problems involving fuzzy preference relations constitute an important class within GDM. Legacy approaches dealing with the latter type can be classified into indirect approaches, which involve deriving group matrix as intermediate step, and direct deduce ranking based on individual rankings. Although work been extensive literature, there still scarcity approaches. In this paper we present approach towards aggregating several set alternatives single weighted alternatives. By mapping pairwise preferences transitions probabilities, are able to derive from stationary distribution stochastic matrix. Interestingly, obtained our method corresponds optimizer Maximum Likelihood Estimation particular Bradley-Terry-Luce model. Furthermore, perform theoretical sensitivity analysis proposed supported by experimental results illustrate GDM concrete numerical example. This opens avenues for solving using elements probability theory, thus, provides sound fundament well plausible statistical interpretation expert opinions