The severity of natural disasters is increasing every year, having an impact on many people’s lives. During the response phase of disasters, airports are important hubs where relief aid arrives while people need to be evacuated to safety. However, the airport often forms a bottleneck in these relief operations because of the sudden need for increased capacity. Limited research is carried out on the operational side of airport disaster management. Experts identify the main problems as first the asymmetry of information between the airport and the incoming flights, and second the lack of resources. The goal of this research is to gain understanding of the effects of incomplete knowledge of incoming flights with different resource allocation strategies on the performance of the cargo handling operations in an airport after a natural disaster event. An agent-based model is created, where realistic offloading strategies with different degrees of information uncertainty are implemented. Model calibration and verification are performed with experts in the field. The model performance is measured by the average turnaround time, which can be split into offloading time, boarding time and the cumulative waiting times. The results show that the effects of one unplanned aircraft are negligible. However, the waiting times and other inefficiencies rapidly increase with the more unplanned aircraft arriving.