LAUSR

In this study, we concentrate on multiple attribute decision-making (MADM) problems in the probabilistic linguistic preference information surroundings based on novel aggregation operators. Considering interrelationships among the multi-input arguments of probabilistic linguistic term sets (PLTSs), we extend dual Muirhead mean (DMM) operators to the probabilistic linguistic preference environment and develop a decision-making approach to deal with probabilistic linguistic MADM (PLMADM) problems. In specific, we define probabilistic linguistic dual Muirhead mean operators, i.e., probabilistic linguistic dual Muirhead mean (PLDMM) operator and probabilistic linguistic weighted dual Muirhead mean (PLWDMM) operator, and further investigate their corresponding propositions, theorems as well as properties. In the light of VIKOR method, a novel decision-making approach for PLMADM problems has been carefully explored. Finally, an application of hospitals selection can fruitfully demonstrate and signify the practicality and feasibility of the proposed decision-making approach.