Genetic algorithms applied to airport ground traffic optimization

Due to air traffic growth and especially hubs development, major European airports can easily become bottlenecks in the global air transportation network. Therefore, accurate models of airport traffic prediction become more and more necessary for ground controllers. In this paper, a ground traffic simulation tool is proposed and applied to Roissy Charles De Gaulle airport. Two global optimization methods, using genetic algorithms at the airport, are developed to minimize taxing time while respecting separation and runway capabilities. In order to compare the efficiency of the different methods, simulations are carried out on a one day traffic sample, and ground delay is correlated to the traffic density at the airport.