An extended HFACS based risk analysis approach for human error accident with interval type-2 fuzzy sets and prospect theory

The traditional Human Factor Analysis and Classification System (HFACS) model has been regarded as one of the most widely used and effective human error accident analysis approaches. However, current HFACS models are insufficient to address accident analysis problem with the high uncertain risk information and inconsistent behavioral preferences. The aim of this paper is to develop an extended HFACS based risk analysis method for human error accident based on interval type-2 fuzzy sets and prospect theory. Firstly, the interval type-2 fuzzy sets are used to express the uncertain evaluation information in the risk analysis process. Secondly, prospect theory is utilized to depict the different risk preferences of experts under uncertain environment. Next, the ordered weighted averaging (OWA) operator for interval type-2 fuzzy number is combined with prospect theory to calculate risk priorities of risk factors. Specially, a ranking method based on possibility mean and variation coefficient is proposed to compare interval type-2 fuzzy numbers. Finally, an illustrative example in marine industry is selected to demonstrate the application of the extended HFACS based risk analysis method. The comparison analysis indicates that the proposed model can achieve relatively reasonable and objective risk evaluation results.