Distance, similarity and entropy measures of dynamic interval-valued neutrosophic soft sets and their application in decision making

In this paper, we introduce the notion of dynamic interval-valued neutrosophic soft sets (DIVNSSs) by embedding the time factor to interval-valued neutrosophic soft sets (IVNSSs). We also present some related set theoretic operations, such as complement, union, intersection, and-product, and or-product. Then, we propose the information measures of DIVNSSs, including the distance, similarity, and entropy measures. And we develop three corresponding decision making methods. In the decision making process, we employ a nonlinear programming model to weight every single time objectively, considering that the importance degrees of every single time are quite different. Further, we put forward a dynamic interval-valued neutrosophic soft aggregation rule to combine the parameter weights evaluated by all experts under every single time. Moreover, we give a numerical example to display the application of the proposed methods in decision making. Finally, we present a sensitivity analysis of the parameter time-degree and a comparative analysis with the methods of IVNSSs and interval-valued neutrosophic sets (IVNSs). The results show the effectiveness and superiority of the proposed method in solving the problem with dynamic inconsistent information.