Joint Air Traffic Flow and Capacity Management via Answer Set Programming
For air traffic management researchers, this work provides a novel joint optimization approach that shows potential benefits over separate optimization, though results are limited to small instances.
This work introduces a joint optimization model for Air Traffic Flow and Capacity Management (ATFCM) using Answer Set Programming (ASP), addressing the gap of separately optimizing trajectories and sector configurations. The ASP model outperforms a SOTA Mixed Integer Programming (MIP) model and remains competitive with heuristics on small instances.
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.