Hesitant fuzzy linguistic term set (HFLTS) can handle the qualitative and hesitant information in multiple attribute decision making (MADM) problems which are widely used in various fields. However, the experts’ evaluation of information is not completely reliable in the situation where their own knowledge background is insufficient. In order to deal with deviations due to incomplete reliability of the evaluation, this paper first proposes the interval probability hesitant fuzzy linguistic variable (IPHFLV), which takes the HFLTS as the evaluation part and adds a novel element-reliability of evaluation, thus can describe the different credibility of information evaluation due to the familiarity of experts with schemes and the differences in knowledge cognition. The operation rules and comparison methods are also illustrated. Particularly, under the inspiration of probability theory, we propose the possibility degree of the IPHFLVs. Then we propose IPHFL-AHP based on the AHP and interval probability hesitant fuzzy linguistic variable. Especially, the general geometric consistency index (G-GCI) based on the unbiased estimator of the variance is presented to measure the consistency and the iterative algorithm is constructed to improve the consistency. We use the possibility degree to calculate the priority vector to acquire the total ranking and introduce the process of IPHFL-AHP. Finally, case study of talent selection is given to illustrate the effectiveness and feasibility of the proposed method.

中文翻译：

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区间概率犹豫模糊语言层次分析法及其在人才选拔中的应用。

犹豫模糊语言术语集（HFLTS）可以处理多属性决策（MADM）问题中的定性和犹豫信息，而该问题已在各个领域广泛使用。但是，在他们自己的知识背景不足的情况下，专家对信息的评估并不完全可靠。为了处理由于评估的不完全性引起的偏差，本文首先提出了区间概率犹豫模糊语言变量（IPHFLV），以HFLTS作为评估部分，并添加了一种新颖的评估元素可靠性，从而可以描述专家对方案的熟悉程度以及知识认知的差异，导致信息评估的信誉不同。还说明了操作规则和比较方法。尤其，在概率论的启发下，我们提出了IPHFLV的可能性程度。然后基于层次分析法和区间概率犹豫模糊语言变量提出了IPHFL-AHP算法。特别是，提出了基于无偏估计量的通用几何一致性指数（G-GCI）来测量一致性，并构造了迭代算法来提高一致性。我们使用可能性度来计算优先级向量以获得总排名，并介绍IPHFL-AHP的过程。最后，以人才选拔为例，说明了该方法的有效性和可行性。特别是，提出了基于无偏估计量的通用几何一致性指数（G-GCI）来测量一致性，并构造了迭代算法来提高一致性。我们使用可能性度来计算优先级向量以获得总排名，并介绍IPHFL-AHP的过程。最后，以人才选拔为例，说明了该方法的有效性和可行性。特别是，提出了基于无偏估计量的通用几何一致性指数（G-GCI）来测量一致性，并构造了迭代算法来提高一致性。我们使用可能性度来计算优先级向量以获得总排名，并介绍IPHFL-AHP的过程。最后，以人才选拔为例，说明了该方法的有效性和可行性。