A consensus-based multi-criteria decision making method integrating GLDS method and quantum probability theory for risk analysis of human errors

Human error is one of the major contributors to adverse events in a socio-technical system. Human factor analysis and classification system (HFACS), a qualitative method, is widely recognized for analyzing human errors from a systematic perspective. To overcome its limitation in quantitative analysis of the risk of human error, many multi-criteria decision making (MCDM) techniques are combined with HFACS. However, most existing MCDM technique-based HFACS methods ignore the uncertainty of experts’ opinions, the consensus among experts, and the interference effect between experts. To this end, a consensus reaching process (CRP)-based linguistic MCDM integrating the gained and lost dominance score (GLDS) method and quantum probability theory (QPT) is proposed to rank human errors’ risk under the HFACS framework. First, 2-tuple linguistic variables are utilized to represent experts’ opinions on human errors’ risk, which can handle experts’ linguistic opinions in an interpretable, accurate, and simple way. Second, a two-stage feedback mechanism-based CRP shifts to identify the human errors whose risk evaluation information is with low consensus degree and improve their consensus, which contributes to high consensus on human errors’ risk prioritization results. Then, GLDS and QPT are combined to derive human errors’ collective risk value, where GLDS considers both the comprehensive and worst performances of human errors and QPT considers the interference effect among experts. Finally, a case study of risk analysis for human errors involved in hospital care is conducted to show the efficiency of the proposed method.