An Integrated Resilience Assessment of Aviation Logistics Networks Using Bayesian Networks: A Case Study of Pandemic-Induced Disruptions at a Major Asian Hub

Abstract

The global aviation logistics network, a critical artery for high-value and time-sensitive supply chains, exhibits significant vulnerability to disruptive shocks. Traditional risk management models, often static and linear, fail to capture the dynamic, non-linear, and cascading nature of such disruptions. This paper proposes a novel methodological framework that integrates the principles of resilience engineering into a Bayesian Network (BN) to quantitatively assess and enhance the resilience of aviation logistics systems. The core innovation lies in structurally embedding the three capacities of resilience—absorptive, adaptive, and restorative—into the BN's topology. A real-world case study of a major Asian air cargo hub (Shanghai Pudong International Airport) during the COVID-19 pandemic is conducted to validate the model. Through predictive, diagnostic, and sensitivity analysis, the model identifies ground staff availability, aircraft redeployment flexibility, and regulatory adaptability as the most critical resilience enablers. The study concludes with strategic, data-driven suggestions for stakeholders to transition from reactive risk mitigation to proactive resilience building, emphasizing investment in human capital, digitalization, and collaborative ecosystem planning.