Interval-valued fuzzy soft set is an extended model of soft set. It is a new mathematical tool that has great advantages in dealing with uncertain information and is proposed by combining soft sets and interval-valued fuzzy sets. The two existing fuzzy decision making algorithms based on interval-valued fuzzy soft sets were given. However, the two existing methods involve the high computational complexity and do not consider the added objects. In order to solve these problems, in this paper, we propose a new efficient decision making algorithm for interval-valued fuzzy soft sets. The comparison results among three methods on one real-life application and 30 synthetic generated datasets show that, the proposed algorithm involves relatively less computation and considers the added objects. Due to relatively less computation, our proposed algorithm has the much higher scalability for the large scale datasets compared with the two existing algorithms. Due to considering the added objects, our proposed algorithm has the much higher flexibility and is beneficial to the extension of interval-valued fuzzy soft set and combination of multiple interval-valued fuzzy soft sets.

区间值模糊软集是软集的扩展模型。它是一种新的数学工具，在处理不确定信息方面具有很大的优势，是通过组合软集和区间值模糊集而提出的。给出了两种基于区间值模糊软集的模糊决策算法。但是，这两种现有方法涉及较高的计算复杂度，并且不考虑添加的对象。为了解决这些问题，本文提出了一种新的有效的区间值模糊软集决策算法。三种方法在一个实际应用中和30个合成生成的数据集之间的比较结果表明，该算法涉及的计算量较小，并考虑了添加的对象。由于计算量相对较少，与现有的两种算法相比，我们提出的算法对大规模数据集具有更高的可扩展性。由于考虑了增加的对象，因此本文提出的算法具有更高的灵活性，有利于区间值模糊软集的扩展和多个区间值模糊软集的组合。