Dependence assessment in human reliability analysis based on cloud model and best-worst method

Abstract

Dependence assessment, which is to assess the dependence level of human errors, is an essential part of human reliability analysis, which could be affected by the complexity and uncertainty of the real world. In this paper, a novel dependence assessment method based on cloud model and best-worst method (BWM) is proposed. Firstly, the influential factors used to measure the dependence level are identified. Then, the social network trust graph of different experts is constructed, and the weights of different experts are determined. Next, the cloud model is adopted to represent the linguistic judgments of experts, where the linguistic judgments are transferred into cloud models, and the assessments of different experts are combined. Finally, based on the dependence level of each factor, the final dependence assessment result is obtained. Two numerical examples are presented to show that the proposed method can effectively provide reliable assessment results under uncertainty. In conclusion, the proposed method provides a novel and effective way for dependence assessment in human reliability analysis.