A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

Abstract

Compared with the existing fuzzy numbers, normal T-spherical fuzzy numbers (NTSFNs) inherit the merits of T-spherical fuzzy numbers and normal fuzzy numbers, and can describe normal distribution phenomena and neutral information at the same time. It not only has a large expression domain, but also has a strong ability to handle indeterminacy and vagueness. In this article, the main purpose is to introduce a novel distance measure and improve the ARAS (Additive Ratio ASsessment) method in the NTSF context to solve the group decision-making problem with combined weight information and make up for the shortcomings of the existing ARAS approaches, for example, correlations between attributes are ignored, the decision process is inflexible, and ARAS has not been extended in the NTSF environment, etc. First, we define a Hamming distance measure with NTSFNs, and propose several Aczel-Alsina operational laws of NTSFNs. Then, we develop the normal T-spherical fuzzy Aczel-Alsina Heronian mean (NTSFAAHM) operator and its weighted form (NTSFAAWHM), and related properties and special cases are discussed. Third, For the NTSF multi-attribute group decision-making (MAGDM) problems, we define the NTSF similarity, improve SWARA(Stepwise Weight Assessment Ratio Analysis) and build MDM (maximizing deviation model) to calculate expert weight and attribute subjective and objective weight respectively on the basis of NTSF Hamming distance. Furthermore, we integrate the NTSFAAWHM operator and Hamming distance with ARAS method to form a novel alternative ranking technique, namely NTSF ARAS-H method. Lastly, a numerical example of investment decision for internet waste clothing recycling platform (IWCRP) is presented to show the feasibility of our methodology, the reliability, effectiveness and rationality are verified via the sensitivity and comparative analysis.