Bayesian network-based resilience assessment of multi-level assignments in automated terminals with "spatio-temporal asynchronous" conduction

The inherent complexity and dynamics of automated terminal multi-level operation systems can lead to uncertain events that can cause changes to the terminal's multi-level operation plan. The resulting temporal and spatial offsets can cause "temporal asynchrony". The "spatio-temporal asynchrony" originates at one node of the operation network and generates a "chain reaction" which, without timely intervention and adjustment, will be transmitted in other parts of the multi-level operation network and become more and more serious over time, causes wider delays and congestion, resulting in multiple negative impacts and making the terminal inefficient overall. This paper attempts to measure the behavior of risk by evaluating the propagation of uncertain events, identifying threats and quantifying the associated capabilities of multilevel operational areas and facilities based on Bayesian networks to improve the port's ability to cope with disruptions and increase resilience. The quantification of resilience is further analyzed through sensitivity analysis and suggestions for improvement are given to port managers.