Ant Colony Systems for Optimizing Sequences of Airspace Partitions

David Gianazza (1) , Nicolas Durand (1)

1 ENAC - Ecole Nationale de l'Aviation Civile

In this paper, we introduce an Ant Colony System algorithm which finds optimal or near-optimal sequences of airspace partitions, taking into account some constraints on the transitions between two successive airspace configurations. The transitions should be simple enough to allow air traffic controllers to maintain their situation awareness during the airspace configuration changes. For the same reason, once a sector is opened it should remain so for a minimum duration. The Ant Colony System (ACS) finds a sequence of airspace configurations minimizing a cost related to the workload and the usage of manpower resources, while satisfying the transition constraints. This approach shows good results in a limited time when compared with a previously proposed $A$ * algorithm on some instances from the french air traffic control center of Aix (East qualification zone) where the $A$ * algorithm exhibited high computation times.00

Mots clés: Air traffic management; Workload; Airspace configuration; Airspace partition; Ant Colony Optimization; Ant Colony System; Tree search; Branch & bound; A* algorithm; A_star algorithm

Domaines: Optimisation et contrôle [math.OC]

David Gianazza, Nicolas Durand. Ant Colony Systems for Optimizing Sequences of Airspace Partitions. AIDA-AT 2020, 1st conference on Artificial Intelligence and Data Analytics in Air Transportation, Feb 2020, Singapour, Singapore. pp.ISBN: 978-1-7281-5381-0, ⟨10.1109/AIDA-AT48540.2020.9049206⟩. ⟨hal-02547511⟩