Joint Air Traffic Flow and Capacity Management via Answer Set Programming
2026-06-22 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors study how to better manage air traffic by balancing the number of flights with the available space in air sectors. They combine two common ways to optimize: adjusting flight paths and changing sector configurations, into one model using Answer Set Programming (ASP). Their experiments show that this new ASP model works better than some existing mathematical methods and is competitive with heuristic approaches for small cases. They also found that some strategies can cause the system to get stuck searching for solutions. Overall, their work explores improving air traffic flow by jointly optimizing key factors.
Air Traffic Flow and Capacity Management (ATFCM)Answer Set Programming (ASP)Mixed Integer Programming (MIP)Demand-Capacity BalancingAircraft Trajectory ManagementSector ConfigurationOptimizationHeuristicsOpenSky NetworkSearch Space Thrashing
Authors
Alexander Beiser, Markus Hecher, Nysret Musliu, Stefan Woltran
Abstract
Operational Air Traffic Flow and Capacity Management (ATFCM) balances flight demand with available sector capacity, to ensure safe and efficient operations. Mathematical models enhance operational ATFCM performance by framing demand-capacity balancing as an optimization problem, maximizing efficiency while adhering to safety constraints. However, SOTA research optimizes the aircraft trajectories (called ATFM) or the sector configuration (called DAC) separately. This leaves a research gap of whether joint optimization of ATFM and DAC can bring benefits. We partially address this limitation by introducing a joint ATFCM model with an encoding in Answer Set Programming (ASP). The ASP implementation is evaluated against two baselines applied to our joint model: a SOTA Mixed Integer Programming (MIP) model and an iterative CASA-based heuristic. Computational experiments utilize an instance generator fitted to historical OpenSky Network flight data. Our results indicate that the ASP model outperforms the MIP model, while ASP remains competitive against heuristics on small instances. Furthermore, while DAC has the largest improvement on solving performance compared to rerouting and delaying, unrestricted variants of DAC or rerouting lead to search space thrashing.