Models for MAGDM with dual hesitant q‐rung orthopair fuzzy 2‐tuple linguistic MSM operators and their application to COVID‐19 pandemic. Issue 8 (16th April 2022) - Naz, Sumera; Akram, Muhammad; Saeid, Arsham Borumand; Saadat, Aniqa
Abstract: 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.