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.

在本文中，我们通过将时间因子嵌入间隔值的中智性软集合（IVNSSs）中，介绍了动态间隔值的中智性软集合（DIVNSSs）的概念。我们还介绍了一些相关的集合理论运算，例如补数，并集，交集，乘积和乘积或乘积。然后，我们提出了DIVNSSs的信息量度，包括距离，相似度和熵量度。并且我们开发了三种相应的决策方法。在决策过程中，考虑到每个时间的重要性程度存在很大差异，我们采用非线性规划模型来客观地加权每个时间。此外，我们提出了一种动态区间值的中智性软聚集规则，以结合所有专家每次评估的参数权重。而且，我们给出一个数值例子来展示所提出的方法在决策中的应用。最后，我们介绍了参数时间度的敏感性分析，并使用IVNSSs和区间值中智集（IVNSs）的方法进行了比较分析。结果表明，该方法在解决动态信息不一致的问题上的有效性和优越性。