A novel multi-attribute decision-making approach based on T-spherical fuzzy Aczel Alsina Heronian mean operators

In the modern era, transportation has also become more expensive due to fluctuations in the prices of fuels and gasses. We demonstrate an efficient transportation system to cope with this critical situation and reduce the impact of rising prices. To serve this purpose, we illustrate the notion of the T-Spherical Fuzzy set (T-SFS) concepts, a more efficient and modified version of an intuitionistic and picture fuzzy set. A T-SFS has an extensive capability to deal with a vague and unpredictable situation of human opinion. In this article, we illustrate the theory of the Heronian mean (HrM) operators to express correlation among different input arguments and also study some dominant operations of Aczel Alsina aggregation tools in the light t-spherical fuzzy (T-SF) information. By employing two different theories, we derive some robust mathematical aggregation approaches based on T-Spherical Fuzzy information, including T-SF Aczel Alsina Heronian mean (T-SFAAHrM) and T-SF Aczel Alsina weighted Heronian mean (T-SFAAWHrM) operators. Furthermore, we also developed some new aggregation operators, such as T-SF Aczel Alsina geometric Heronian mean (T-SFAAGHrM) and T-SF Aczel Alsina weighted geometric Heronian mean (T-SFAAWGHrM) operators with dominant characteristics and properties. To show the robustness and feasibility of our derived approaches, we evaluated a multi-attribute decision-making (MADM) technique under the system of T-SF information. An experimental case study illustrates how to choose an appropriate optimal option and investigate a reasonable source of transportation by employing our derived approaches. To reveal the supremacy of currently proposed approaches, we established a comprehensive comparative analysis to contrast the results of proposed approaches with prevailing theories in the literature.