Dynamic Air Traffic Planning by Genetic Algorithms

In the past, the first way to reduce the congestion of the Air Traffic Control System was to modify the structure of the airspace in order to increase the capacity (increasing the number of runways, increasing the number of sectors by reducing their size). This method has a limit due to the cost involved by new runways and the way to manage traffic in too small sectors (a controller needs a minimum amount of airspace to solve conflicts). The other way to reduce congestion is to modify the flight plans in order to adapt the demand to the available capacity. So, to reduce congestion, demand has to be spread in spatial and time dimension (route-slot allocation). Our research addresses the general timeroute assignment problem using a static and a dynamic approach. A state of the art of the existing methods shows that this general biallocation problem is usually partially treated and the whole problem remains unsolved due to the induced complexity. GAs are then adapted to the problem. A sector congestion measure has been developed which gather the major control workload indicators. This measure is then computed for each proposed planning by referring to an off-line simulation. New problem-based stochastic operators have been developed and successfully applied on real instances of the problem.