Hesitant fuzzy partitioned Maclaurin symmetric mean aggregation operators in multi-criteria decision-making

Abstract

Abstract A hesitant fuzzy set, enabling the membership of an element to be a set of various possible values, is highly helpful in describing people’s uncertainty in everyday life. Hesitant fuzzy aggregation operators are the standard mathematical tools for combining many inputs according to predefined criteria into a single result. The classic hesitant fuzzy aggregation operator-based approaches have been criticized because of the ignorance of criteria classification. In this work, we develop the conception of the hesitant fuzzy partitioned Maclaurin symmetric mean and hesitant fuzzy weighted partitioned Maclaurin symmetric mean operators spurred by the partitioned Maclaurin symmetric mean. Afterward, we analyze several features and peculiar instances of the formulated operators. A novel multiple criteria decision-making (MCDM) technique is propounded on the documented hesitant fuzzy weighted partitioned Maclaurin symmetric mean operator; the MCDM method chooses the optimal alternative from several alternatives. A case study of the best location selection for hospital construction is addressed to showcase the practicability of the presented technique. Eventually, we illustrate the devised approach is more widespread and efficacious than prevailing approaches via comparative and sensitive analyses.