An Aczel-Alsina aggregation-based outranking method for multiple attribute decision-making using single-valued neutrosophic numbers

Abstract

The “single-valued neutrosophic set (SVNS)” is used to simulate scenarios with ambiguous, incomplete, or inaccurate information. In this article, with the aid of the Aczel-Alsina (AA) operations, we describe the aggregation operators (AOs) of SVNSs and how they work. AA t-norm (t-NM) and t-conorm (t-CNM) are first extended to single-valued neutrosophic (SVN) scenarios, and then we introduce several novel SVN operations, such as the AA sum, AA product, AA scalar multiplication, and AA exponentiation, by virtue of which we generate a few useful SVN AOs, for instance, the SVN AA weighted average (SVNAAWA) operator, SVN AA order weighted average (SVNAAOWA) operator, and SVN AA hybrid average (SVNAAHA) operator. Next, we create distinct features for such operators, group numerous exceptional cases together, and study the relationships between them. Following that, we created a way for “multiple attribute decision making (MADM)” in the SVN context using the SVNAAWA operator. We provided an illustration to substantiate the appropriateness and, additionally, the productiveness of the produced operators and strategy. Besides this, we contrasted the suggested strategy to the given procedures and conducted a comprehensive analysis of the new framework.