The purpose of this research study is to extend the multi-attribute group decision-making (MAGDM) methods to hesitant fuzzy soft set (HFS-set), hesitant fuzzy soft topology (HFS-topology) and HFS-Hausdorff spaces in group decision-making environment, as HFS-set is more superior tool to capture vagueness, hesitancy and incompleteness in individual evaluations. In order to obtain optimal decisions in MAGDM, we present two algorithms based on hesitant fuzzy soft set and hesitant fuzzy soft topology. Lastly, we present MAGDM method by using HFS-Hausdorff space to deal with hesitancy and uncertainty. The developed methods have the ability to solve MADGM problems in which the assessment information on available alternatives, provided by the experts, is presented by hesitant fuzzy soft sets. Furthermore, the efficiency of proposed algorithms is shown by applying them to the real-world problems. We use reduct, optimum reduct, aggregate HFS-sets and weight vector according of given alternatives, priority of the attributes and customer demand for best MAGDM in the selection of car.

这项研究的目的是将多属性群决策方法（MAGDM）扩展到群决策中的犹豫模糊软集（HFS-set），犹豫模糊软拓扑（HFS-topology）和HFS-Hausdorff空间HFS-set是更出色的工具，可以捕获单个评估中的模糊性，犹豫性和不完整性。为了在MAGDM中获得最佳决策，我们提出了两种基于犹豫模糊软集和犹豫模糊软拓扑的算法。最后，我们提出了利用HFS-Hausdorff空间处理不确定性和不确定性的MAGDM方法。所开发的方法具有解决MADGM问题的能力，其中由专家提供的有关可用替代方案的评估信息由犹豫的模糊软集提供。此外，通过将其应用于实际问题来显示所提出算法的效率。我们根据给定的替代方案，属性的优先级和客户对最佳MAGDM的需求，在选择汽车时使用归约，最优归约，总HFS集和权重向量。