Aczel-Alsina power average aggregation operators of Singlevalued Neutrosophic under confdence levels and theirapplication in multiple attribute decision making

Abstract

 In decision making scenarios, dealing with imprecise information through extensions of fuzzy sets
 is crucial. Among these extensions, single valued neutrosophic set (SVNS) are especially effective at managing
 and interpreting such imprecise data. In the current study, decision makers confidence levels, derived from their
 familiarity with the assessed objects, are combined with the primary data within a neutrosophic framework.
 This paper focuses on developing innovative confidence single valued neutrosophic (SVN) aggregation operators
 (AO) that utilize the recently developed Aczel-Alsina (AA) operational laws and power AO (PAO) to capture
 the interrelationships among aggregated single valued neutrosophic numbers (SVNN). Specifically, it introduces
 new confidence SVNAA power average AO, namely, confidence SVNAA power weighted and ordered weighted
 average AO, which integrate the decision maker familiarity with the aggregated arguments. To evaluate the
 effectiveness of the proposed operators, we perform a comprehensive examination of their desirable properties.
 Also, we use these suggested operators to establish a innovative approach for SVN multi attribute decision
 making problems (MADM). A demonstrative example of strategic suppplier selection is provided to validate
 the proposed approach and highlight its practicality and effectiveness.