Airspace congestion smoothing by multi-objective genetic algorithm - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2005

Airspace congestion smoothing by multi-objective genetic algorithm

Résumé

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.
Fichier principal
Vignette du fichier
Delahaye_SAC2005.pdf (164.27 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01004145 , version 1 (11-07-2014)

Identifiants

Citer

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⟩
112 Consultations
634 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More