An improved CREAM model based on Deng entropy and evidence distance
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The cognitive reliability and error analysis method (CREAM), a widely used human reliability analysis (HRA) approach, quantifies the cognitive failure probability (CFP) in specific situations. In the application of CREAM, the levels of common performance conditions (CPCs) are often determined based on domain experts’ judgments. This process may include uncertainty and inconsistency. How to effectively deal with such information to achieve reasonable outcomes is a critical issue. In this article, an improved CREAM model based on Deng entropy and evidence distance is proposed to address this issue. Firstly, the Dempster–Shafer evidence theory (DSET) is used to model the uncertainty inherent in experts’ judgments. Secondly, the uncertainty and inconsistency of the experts’ judgments are measured by Deng entropy and the evidence distance, respectively. Based on this, an optimized method to fuse experts’ judgments is proposed. Finally, several numerical examples and a case study are provided to demonstrate the effectiveness of the proposed method
Abstract
The cognitive reliability and error analysis method (CREAM), a widely used human reliability analysis (HRA) approach, quantifies the cognitive failure probability (CFP) in specific situations. In the application of CREAM, the levels of common performance conditions (CPCs) are often determined based on domain experts’ judgments. This process may include uncertainty and inconsistency. How to effectively deal with such information to achieve reasonable outcomes is a critical issue. In this article, an improved CREAM model based on Deng entropy and evidence distance is proposed to address this issue. Firstly, the Dempster–Shafer evidence theory (DSET) is used to model the uncertainty inherent in experts’ judgments. Secondly, the uncertainty and inconsistency of the experts’ judgments are measured by Deng entropy and the evidence distance, respectively. Based on this, an optimized method to fuse experts’ judgments is proposed. Finally, several numerical examples and a case study are provided to demonstrate the effectiveness of the proposed method