Novel linguistic q-rung orthopair fuzzy Aczel-Alsina aggregation operators for group decision-making with applications

Abstract

In this article, we presented two novel approaches for group decision-making (GDM) that were derived from the initiated linguistic $ q $-rung orthopair fuzzy Aczel-Alsina weighted arithmetic (L$ q $-ROFAAWA) aggregation operator (AgOp) using linguistic $ q $-rung orthopair fuzzy numbers (L$ q $-ROFNs). To introduce these GDM techniques, we first defined new operational laws for L$ q $-ROFNs based on Aczel-Alsina $ t $-norm and $ t $-conorm. The developed scalar multiplication and addition operations of L$ q $-ROFNs addressed the limitations of operations when $ q = 1 $. The first proposed GDM methodology assumed that both experts' weights and attribute weights were fully known, while the second technique assumed that both sets of weights were entirely unknown. We also discussed properties of L$ q $-ROFNs under the L$ q $-ROFAAWA operators, such as idempotency, boundedness, and monotonicity. Furthermore, we solved problems related to environmental and economic issues, such as ranking countries by air pollution, selecting the best company for bank investments, and choosing the best electric vehicle design. Finally, we validated the proposed GDM approaches using three validity tests and performed a sensitivity analysis to compare them with preexisting models.

Keywords

• linguistic $ q $-rung orthopair fuzzy set
• aggregation operator
• aczel-alsina $ t $-norm
• air pollution
• sensitivity analysis
• decision-making