Multiple-Attribute Decision Making Based on Novel Single Valued Neutrosophic Aczel-Alsina Power Muirhead Mean Operators - Peide Liu; Qaisar Khan; Rashid Ali; Hidayat Ullah; Rashid Jan (Neutrosophic Sets and Systems)
Neutrosophic multiple-attribute decision making (MADM) is one of the major study areas in ambiguous and inconsistent decision-making problems. The single-valued neutrosophic number (SVNN), which is an expansion of the fuzzy number, intuitionistic fuzzy number, can handle difficulties involving a huge quantity of erroneous, partial, and inconsistent information. To address the multi-criteria decision-making problems, novel aggregation operators (AGOs) are proposed in this article considering the benefits of the SVNN. The new AGOs consider the interrelationship involving any number of input data and can remove the effect of unreliable data. This article adds the Aczel-Alsina (AA) operations to increase the flexibility of the new AGOs. This study proposes the single-valued neutrosophic Aczel-Alsina power Muirhead mean (SVNAAPMM) operator, single-valued neutrosophic Aczel-Alsina power dual Muirhead mean (SVNAAPDMM) operator which combines the Aczel-Asina operational rules with the power average/geometric operator and the Muirhead/ dual Muirhead mean operators. Some core characteristics and special cases with respect to the parameters are explored and it is found that various existing AGOs are special cases of the newly initiated AGOs. Further, weighted form these AGOs are introduced. Then, we set up the MADM technique using the two AGOs that are suggested to solve MADM problems. We then give a numerical example about strategic supplier’s selection and compare it to other related MADM techniques already in use in the SVNN information to demonstrate the effectiveness and appropriateness of the anticipated technique.