Human reliability analysis (HRA) is a proactive approach to model and evaluate human systematic errors, and has been extensively applied in various complicated systems. Dependence assessment among human errors plays a key role in the HRA, which relies heavily on the knowledge and experience of experts in real-world cases. Moreover, there are ofthen different types of uncertainty when experts use linguistic labels to evaluate the dependencies between human failure events. In this context, this paper aims to develop a new method based on linguistic hesitant fuzzy sets and the technique for human error rate prediction (THERP) technique to manage the dependence in HRA. This method handles the linguistic assessments given by experts according to the linguistic hesitant fuzzy sets, determines the weights of influential factors by an extended best-worst method, and confirms the degree of dependence between successive actions based on the THERP method. Finally, the effectiveness and practicality of the presented linguistic hesitant fuzzy THERP method are demonstrated through an empirical healthcare dependence analysis.

人为可靠性分析（HRA）是一种主动建模和评估人为系统错误的方法，已广泛应用于各种复杂系统。人为错误之间的依赖性评估在 HRA 中起着关键作用，它在很大程度上依赖于专家在现实世界案例中的知识和经验。此外，当专家使用语言标签来评估人为故障事件之间的依赖关系时，存在不同类型的不确定性。在此背景下，本文旨在开发一种基于语言犹豫模糊集和人为错误率预测 (THERP) 技术的新方法来管理 HRA 中的依赖性。该方法根据语言犹豫模糊集处理专家给出的语言评估，通过扩展的 best-worst 方法确定影响因素的权重，并基于 THERP 方法确认连续动作之间的依赖程度。最后，通过实证医疗保健依赖性分析证明了所提出的语言犹豫模糊 THERP 方法的有效性和实用性。