Modeling Truck Congestion in Landside Air Cargo Processes

Truck congestion results from temporary capacity overloads that can cause severe delays in logistical processes. These delays directly impact supply chain cost, performance, and environmental emissions. However, existing solutions often focus on scheduled arrivals, which are not feasible in this context. This paper presents a novel integrated approach to modeling and mitigating uncoordinated truck congestion in air cargo operations. We develop a hybrid simulation methodology that combines an analytical queueing network model with a detailed discrete event simulation. This allows us to capture the complex and stochastic nature of air cargo processes while efficiently evaluating both infrastructure and operational improvements. Our key innovation is simultaneously considering capacity expansions, fast lanes, and sequencing rules through an integrated perspective. We evaluate multiple scenarios using real operational data and seven performance measures. Our results demonstrate that while infrastructure investments provide the largest reductions in congestion, carefully designed fast lanes and sequencing rules offer substantial benefits at a lower cost. This research provides air cargo managers with data-driven insights to optimize operations, reduce congestion, and improve sustainability through an innovative modeling approach tailored to the unique challenges of uncoordinated truck arrivals in air cargo handling.