MAAIJul 7, 2025

Effects of Unplanned Incoming Flights on Airport Relief Processes after a Major Natural Disaster

arXiv:2507.05150v11 citationsh-index: 18Aerospace
Originality Incremental advance
AI Analysis

This addresses operational inefficiencies in airport disaster management for relief agencies, but it is incremental as it builds on limited prior research in this area.

This research tackled the problem of airport bottlenecks in disaster relief by modeling the impact of unplanned incoming flights on cargo handling operations, finding that while one unplanned aircraft has negligible effects, waiting times increase significantly with more such arrivals.

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

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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