Airspace congestion smoothing by multi-objective genetic algorithm

Daniel Delahaye (1) , Sofiane Oussedik (2) , Stéphane Puechmorel (3)

1 DGAC - Direction Générale de l'Aviation Civile
2 ILOG SA [Gentilly]
3 ENAC - Ecole Nationale de l'Aviation Civile

Air traffic control systems become more and more congested due to the increase of demand. One way to reduce this congestion is to modify the flight plans (slot of departure and route of aircraft) in order to adapt the demand to the available capacity. This paper addresses the general time-route assignment problem which can be stated as follows: one has to find an optimal time of departure and an optimal route for all the aircraft involved in the considered airspace, in order to minimize the associated congestion and the induced delay. This problem is a multi-objective NP_Hard problem. We perform our research on the application of multi-objective stochastic methods on real traffic data without using the flow network concept, but by simulating the flight of each aircraft. The first results show that our approach is able to reduce congestion of the French airspace by a factor 2.

Domaines: Optimisation et contrôle [math.OC]

Daniel Delahaye, Sofiane Oussedik, Stéphane Puechmorel. Airspace congestion smoothing by multi-objective genetic algorithm. SAC 2005, 20th Annual ACM Symposium on Applied Computing, Mar 2005, Santa Fe, United States. pp 907-912, ⟨10.1145/1066677.1066887⟩. ⟨hal-01004145⟩