The Hesitant Fuzzy Linguistic TOPSIS Method Based on Novel Information Measures
Abstract
Hesitant fuzzy linguistic term sets (HFLTSs) have attracted lots of attention recently due to their distinguished power and efficiency for dealing with multi-criteria decision making. To extend the applicability of HFLTSs, this paper first presents a more concise comparison formula of hesitant fuzzy linguistic term sets based on probability criterion of uniform distribution and develops novel distance measures considering the hesitance degree. Next, an aggregation scheme is designed to integrate OWD measure into the TOPSIS analysis procedure, in which we calculate different criterion weights according to the distance between criterion values and positive and negative ideal solutions. The weighted distances between the alternatives and positive and negative ideal solutions are calculated. Then the relative closeness degree to the positive ideal solution is calculated to rank all alternatives. Finally, an example is given to illustrate the capabilities and validation of the proposed algorithm.
