3D Sectors Design by Genetic Algorithm Towards Automated Sectorisation

Marina Sergeeva (1) , Daniel Delahaye (1) , Catherine Mancel (2) , Leila Zerrouki (3) , Nick Schede (3)

1 MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
2 ENAC - Ecole Nationale de l'Aviation Civile
3 Eurocontrol

The aim of this work is to develop a research prototype to support the validation of new airspace sector design methodology. To do this, an algorithm has been developed that manages main features of the sector design process. The proposed method is based on a mathematical modeling and heuristic optimization techniques. In order to run this algorithm efficiently a pre-processing step has been proposed, which creates an initial division of the airspace into Voronoi cells using k-means clustering algorithm. Then, due to the induced combinatorial complexity, a stochastic optimization algorithm based on artificial evolution has been applied to solve the sectorisation problem. An evaluation of the algorithm is presented as well, with a comparison to existing sectorisation with the support of the operational expertise.

Mots clés: genetic algorithm; k-means clustering; esctor design

Domaines: Optimisation et contrôle [math.OC]

Marina Sergeeva, Daniel Delahaye, Catherine Mancel, Leila Zerrouki, Nick Schede. 3D Sectors Design by Genetic Algorithm Towards Automated Sectorisation. SID 2015, 5th SESAR Innovation days, Dec 2015, Bologna, Italy. ⟨hal-01240312⟩