An Aczel-Alsina T-Spherical Fuzzy Framework for the Electric Vehicle Selection

Abstract

Among many other selection criteria, multi-attribute decision-making is a tool used in various industries, including risk control, numerical examination, cybercrime investigation, networking, etc. Aczel-Alsina aggregation operators are suitable to mitigate the effects of inconsistent data. In this article, we present a few novel methodologies to account for T-spherical fuzzy set data using Aczel-Alsina aggregation techniques. The T-spherical fuzzy Aczel-Alsina weighted average operator and the T-spherical fuzzy Aczel-Alsina ordered weighted average are two examples of these novel, unique approaches. The T-spherical fuzzy Aczel-Alsina weighted geometric and T-spherical fuzzy Aczel-Alsina ordered weighted geometric operators are novel strategies that we also introduce. We look at a few unique situations and noteworthy properties to show the persistence and effectiveness of the techniques that are provided. A method for leveraging the T-spherical fuzzy information system to resolve the electric vehicle selection problem is also described. Due to the high cost of petrol and the current financial difficulties faced by middle-class households, this problem is chosen. An experimental case study is also built. To calculate the accuracy and authority of the present developments, we contrast the outcomes of formerly used methods with the aggregation operators that are presently available.