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A large group decision making approach for dependence assessment in human reliability analysis

机译：人类可靠性分析中依赖评估的大型决策方法

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Human reliability analysis (HRA) is a systematic technique to assess human contribution to system risk and has been widely used in diverse complex systems. Dependence assessment among human errors is an important activity in HRA, which depends heavily on domain experts' knowledge and experience. Normally, it is common for experts to give their judgments using linguistic labels and different types of uncertainties may exist in the dependence assessments. Additionally, the existing dependence assessment methods are limited to small-scale expert groups, which reduce the accuracy of dependence analysis with the increasing complexity of high risky systems. In this article, we develop a large group dependence assessment (LGDA) model based on interval 2-tuple linguistic variables and cluster analysis method to manage the dependence in HRA. Further, we propose an extended Muirhead mean operator to determine the dependence levels between consecutive operator actions. Finally, an empirical healthcare dependence analysis is taken as an example to illustrate the effectiveness and practicality of our proposed LGDA approach.
机译：人类可靠性分析（HRA）是一种评估人类对系统风险的贡献的系统技术，已广泛用于各种复杂的系统中。人为错误中的依存关系评估是HRA中的一项重要活动，它在很大程度上取决于领域专家的知识和经验。通常，专家通常使用语言标签来做出判断，依赖评估中可能存在不同类型的不确定性。另外，现有的依赖性评估方法仅限于小型专家组，这会随着高风险系统的复杂性降低而降低依赖性分析的准确性。在本文中，我们基于区间2元组语言变量和聚类分析方法开发了大型群体依赖评估（LGDA）模型，以管理HRA中的依赖。此外，我们提出了一个扩展的Muirhead均值算子来确定连续算子动作之间的依赖程度。最后，以医疗保健经验依赖分析为例，说明了我们提出的LGDA方法的有效性和实用性。

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