Group Decision-Making Framework with Generalized Orthopair Fuzzy 2-Tuple Linguistic Information

Many decision-making problems in real-life scenarios depend on how to deal with uncertainty, which is typically a big challenge for decision-makers (DMs). Mathematical models are not common, but where the complexity is not usually probabilistic, various models emerged along with fuzzy logic and linguistic fuzzy approach. In the linguistic environment, multiple attribute group decision-making (MAGDM) is an essential part of modern decision-making science, and information aggregation operators play a crucial role in solving MAGDM problems. The notion of generalized orthopair fuzzy sets (GOFSs) (also known as q-rung orthopair fuzzy sets) serves as an extension of intuitionistic fuzzy sets $$(q=1)$$ ( q = 1 ) and Pythagorean fuzzy sets $$(q=2)$$ ( q = 2 ) . The generalized orthopair fuzzy 2-tuple linguistic (GOFTL) set provides a better way to deal with uncertain and imprecise information in decision-making. The Maclaurin symmetric mean (MSM) aggregation operator is a useful tool to model the interrelationship between multi-input arguments. In this chapter, we generalize the traditional MSM to aggregate GOFTL information. Firstly, the GOFTL Maclaurin symmetric mean (GOFTLMSM) and the GOFTL weighted Maclaurin symmetric mean (GOFTLWMSM) operators are proposed along with desirable properties and some special cases. Furthermore, the GOFTL dual Maclaurin symmetric mean (GOFTLDMSM) and GOFTL weighted dual Maclaurin symmetric mean (GOFTLWDMSM) operators with some properties and cases are presented. An efficient approach is developed to tackle the MAGDM problems within the GOFTL framework based on the GOFTLWMSM and GOFTLWDMSM operators. Finally, a numerical illustration regarding the selection of the most preferable supplier(s) in enterprise framework group (EFG) of companies is given to demonstrate the application of the proposed approach and exhibit its viability.