Multi-attribute group decision-making method based on weighted partitioned Maclaurin symmetric mean operator and a novel score function under neutrosophic cubic environment

Abstract

Neutrosophic cubic set is the generalized version of neutrosophic sets and interval neutrosophic sets. It can deal with the complex information by combining the neutrosophic set (NS) and cubic set (CS). Maclaurin symmetric mean operator is a classical aggregation operator in modern information fusion theory. Partitioned MSM operator can reflect the interrelationships among attributes in the same partition, but the attributes in different partitions are irrelevant. In order to efficiently gather NCS information where the attributes in the same partition are relevant but the attributes in different partitions are irrelevant, we extend the PMSM operator to neutrosophic cubic environment and define the neutrosophic cubic partitioned Maclaurin symmetric mean (NCPMSM) operator and neutrosophic cubic weighted partitioned Maclaurin symmetric mean operator. Later, in order to overcome the drawbacks of the existing score functions and effectively distinguish two NCSs, we define a novel score function of NCS. Next, based on NCWPMSM operator and the novel score function, we develop a multi-attribute group decision-making method. Finally, we give an example of supplier selection to illustrate the usefulness of the proposed MAGDM. At the same time, a comparative analysis is to show the effectiveness and advantages of the proposed method compared with the existing methods.