基于FMEA-FBWM-RBN的中国粮食进出口海运风险评估OA

This study proposes an integrated risk-assessment framework based on FMEA-FBWM-RBN to address multi-source uncertainties in China's seaborne grain import-export supply chain,enabling key-node risk identification,consistency-aligned weighting,and scenario-based inference to assist resilience building and poli-cy preparedness.It systematically identify 19 failure modes across five dimensions,namely,extreme weather,traffic congestion,corridor security,politics&law,and cargo&ships;use the fuzzy best-worst method(FB-WM)to aggregate expert judgments and handle subjectivity and vagueness,and implement causal propagation and scenario inference via a risk Bayesian network(RBN)in GeNIe.Empirical analysis yields the crisp risk values(CV)for each failure mode and category,from which the key drivers affecting grain maritime safety are identified,including the nature of grain,policy adjustments in canals and straits,as well as export country poli-cy restrictions and bans,etc.Sensitivity analysis further validates the model's robustness and interpretability.