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

Ahmed Khassiba (1) , Daniel Delahaye (2)

1 Capgemini [Toulouse]
2 OPTIM - ENAC - Equipe Optim

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.

Mots clés: demand-capacity balancing hyper-heuristics simulated annealing; demand-capacity balancing; hyper-heuristics; simulated annealing

Domaines: Intelligence artificielle [cs.AI]

Ahmed Khassiba, Daniel Delahaye. Simulated-Annealing Hyper-Heuristic for Demand-Capacity Balancing in Air Traffic Flow Management. SESAR Innovation Days, Dec 2022, budapest, Hungary. ⟨hal-03907362⟩