Situational ME-LOWA Aggregation Model for Evaluating the Best Main Battle Tank

OWA (ordered weighted averaging) operators have been extensively adopted to handle MCDM problems. However, previous operators are usually independent of their situations and cannot reflect change in situations. Besides, the evaluated data with linguistic preferences should be aggregated with feasible operators. To resolve above problems, a linguistic MCDM aggregation model with situational ME-LOWA operators is proposed in this study. The proposed model is applied to evaluate the best main battle tank, and experimental results indicate that the proposed model can deal the situational group MCDM problems with linguistic preferences.