Probabilistic linguistic WASPAS method for patients’ prioritization by developing prioritized Maclaurin symmetric mean aggregation operators

Due to the fuzziness of healthcare, the probabilistic linguistic term set (PLTS) is the appropriate technique to assist health experts express their evaluations in the patient prioritization problem. This paper proposes a new method based on the integration of prioritized averaging (PA) and Maclaurin symmetric mean (MSM) operator within the probabilistic linguistic environment. According to the prioritization relationship of the experts and the criteria set, we employ the PA to define the priority degrees. Keeping to the merits of the PA and MSM operators, we develop some novel aggregation operators for probabilistic linguistic information. Particularly, we develop the probabilistic linguistic prioritized Maclaurin symmetric mean (PLPMSM) and probabilistic linguistic prioritized dual Maclaurin symmetric mean (PLPDMSM) operators. Some accompanying properties and useful remarks of these operators are examined. Moreover, we formulate a new ranking procedure for probabilistic linguistic multi-criteria group decision making (PLMCGDM) based on the extended WASPAS method. A patients’ prioritization problem is analyzed to depict the usefulness and robustness of the proposed method.