Models for MAGDM with dual hesitant q -rung orthopair fuzzy 2-tuple linguistic MSM operators and their application to COVID-19 pandemic.

In this article, we introduce dual hesitant q -rung orthopair fuzzy 2-tuple linguistic set (DH q -ROFTLS), a new strategy for dealing with uncertainty that incorporates a 2-tuple linguistic term into dual hesitant q -rung orthopair fuzzy set (DH q -ROFS). DH q -ROFTLS is a better way to deal with uncertain and imprecise information in the decision-making environment. We elaborate the operational rules, based on which, the DH q -ROFTL weighted averaging (DH q -ROFTLWA) operator and the DH q -ROFTL weighted geometric (DH q -ROFTLWG) operator are presented to fuse the DH q -ROFTL numbers (DH q -ROFTLNs). As Maclaurin symmetric mean (MSM) aggregation operator is a useful tool to model the interrelationship between multi-input arguments, we generalize the traditional MSM to aggregate DH q -ROFTL information. Firstly, the DH q -ROFTL Maclaurin symmetric mean (DH q -ROFTLMSM) and the DH q -ROFTL weighted Maclaurin symmetric mean (DH q -ROFTLWMSM) operators are proposed along with some of their desirable properties and some special cases. Further, the DH q -ROFTL dual Maclaurin symmetric mean (DH q -ROFTLDMSM) and weighted dual Maclaurin symmetric mean (DH q -ROFTLWDMSM) operators with some properties and cases are presented. Moreover, the assessment and prioritizing of the most important aspects in multiple attribute group decision-making (MAGDM) problems is analysed by an extended novel approach based on the proposed aggregation operators under DH q -ROFTL framework. At long last, a numerical model is provided for the selection of adequate medication to control COVID-19 outbreaks to demonstrate the use of the generated technique and exhibit its adequacy. Finally, to analyse the advantages of the proposed method, a comparison analysis is conducted and the superiorities are illustrated.