Simulated-Annealing Hyper-Heuristic for Demand-Capacity Balancing in Air Traffic Flow Management

Very critical convective weather leads to sharp drop in air-traffic en-route sector capacity keen on creating severe demand-capacity imbalances, called also hotspots. Following the knock-on effect, these imbalances spread across the network, causing the so-called netspots. The problem of demand-capacity hotspot/netspot mitigation with demand-side measures aims at solving these imbalances using ground delays and reroutings, while minimizing the total delay at arrival. A simulated-annealing hyper-heuristic relying on efficient neighborhood operators is proposed. A study on the combination of different delay-based and rerouting-based neighborhood operators is conducted. The best combination is used to solve a large-scale and challenging instance, in a relatively short computation time.