Hybrid method for holistic air traffic demand and capacity balancing optimisation based on sector complexity

This paper presents a new hybrid method, based on simulated annealing and dynamic programming, tailored to solve a Demand and Capacity Balancing (DCB) problem that overcomes the limitations of the current Air Traffic Flow and Capacity Management (ATFCM) system by: (a) the introduction of complexity metrics (instead of entry counts) in order to measure the traffic load; (b) the better consideration of the airspace users’ preferences, allowing the possibility of submitting alternative trajectories to avoid congested airspace; and (c) the holistic integration of the demand and capacity management into the same optimisation problem. This new method is compared with the state-of-the-art method for MILP providing better performance principally when the difficulty of the problem increases. Finally, the proposed method is applied to a real-scale scenario, demonstrating its practical applicability in real-world cases. • Complexity metrics addressed the limitations of entry/occupancy counts. • The proposed demand and capacity balancing model includes complexity metrics. • The proposed Hybrid method is based on simulated annealing and dynamic programming. • The Hybrid method overperforms exact methods and can deal with real-scale scenarios. • The model improves the current air traffic flow management system.