Neutrosophic cubic Maclaurin symmetric mean aggregation operators with applications in multi-criteria decision-making

The Maclaurin symmetric mean (MSM(m)) operator refers to a classical mean type of aggregation operator fusing information. The prominent property exhibited by the MSM(m) operator is to capture the interrelationship among multiple arguments. In this study, a wide range of MSM(m) operators are extended (e.g., the MSM(m), the generalized MSM(m), and the generalized geometric MSM(m)) to the neutrosophic cubic fuzzy environment, that is, the neutrosophic cubic MSM(m) operator (NCMSM(m)), the neutrosophic cubic generalized MSM(m) operator (NCGMSM((m,p1, p2, ..., pm))), as well as the neutrosophic cubic geometric MSM(m) operator (NCG(eo)MSM((m,p1, p2, ..., pm))). Subsequently, given the significance of the criteria, the weighted neutrosophic cubic MSM(m) operator, the weighted NCGMSM((m,p1, p2, ..., pm)) and the weighted NCG,MSM(m,p1, p2, ..., pm) are defined over the neutrosophic cubic sets. An in-depth investigation is conducted on several desirable properties and special cases of the mentioned operators as well as the relationship between the parameters on NCMSM(m) operator and its aggregating result. Moreover, a novel multi-criteria decision-making approach is developed by complying with the defined aggregation operators. Last, a numerical example and a comparative analysis, as well as sensitive analyses, are presented to verify the effectiveness of the proposed approaches and the robustness of the output result.