An Improved Large Neighborhood Search for Network-Level Airport Slot Allocation Optimization

Airports worldwide face a significant gap between capacity and demand. As a short-term solution, the International Air Transport Association has implemented schedule coordination rules at busy airports. While the complex slot allocation problem has been studied extensively, most research focuses on single airports, overlooking interactions with other airports. Limited studies on network-level allocation lack scalability for real-world problems. This study develops an optimization model to enhance schedule synchronization among airports with connection dependencies. The model integrates differentiated slot priorities and flight connection requirements under capacity constraints, while introducing unassigned request variables to address request overflow scenarios. Accordingly, an enhanced large neighborhood search algorithm was developed with three proposed spatial-temporal neighborhood structures specifically designed for the model characteristics. Validated through an extensive case study involving 4,695 requests across six hub airports, the algorithm achieves solutions within a 1.7% optimality gap while reducing computation time by up to 64%.