An operational risk analysis model for container shipping systems considering uncertainty quantification

Different uncertain factors obstruct the analysis of operational risks in container shipping, especially those rooted in the subjectivity of multiple risk assessments and their aggregation. This paper proposes a risk analysis model featuring a quantification of the uncertainty. Bayesian probability theory is employed to quantify the risk magnitude, while a dedicated module to handle uncertainty is enabled by Evidential Reasoning and a set of three uncertainty indicators, including expert ignorance, disagreement among experts, and polarization of their assessments. The situation of risk is diagnosed by risk ranking and visualized by risk mapping, using both Risk Magnitude Index and Uncertainty Index. The functionality of the proposed model in identifying critical and uncertain risks was demonstrated in an organizational-scale case study, followed by an examination of validity criteria and a sensitivity test. The case study reveals the physical flow as the dominant origin of high-ranking risks with potential significant consequences such as piracy, dangerous cargoes, and maritime accidents; while information and financial operational risks are more uncertain, especially cargo misdeclaration and unexpected rises of fuel costs