A Decision-Making Method for Selecting the Natural Detection System in High-Speed Railways

This paper investigates multi-attribute group decision-making (MAGDM) model to solve the selection of the natural detection system in High-speed railways. In this model, the attribute values provided by decision makers (DMs) are in the form of cubic intuitionistic fuzzy numbers (CIFNs). When aggregating CIFNs, cubic intuitionistic fuzzy aggregation operators (AOs) are needed. However, existing AOs of CIFNs fail to consider the interrelationship among multiple input variables, and they cannot effectively deal with DMs’ extreme evaluation values, either. Hence, this paper proposes novel cubic intuitionistic fuzzy AOs based on power average operator and Muirhead mean, i.e., cubic intuitionistic fuzzy power weighted average operator and cubic intuitionistic fuzzy power Muirhead mean operator. In addition, the weight of the DMs can be calculate based on the social trust network and a novel method is proposed to obtain the weights of the attribute when it is unknown. Finally, a numerical example was performed to illustrate the application of the proposed method.