The Air Traffic Flow Management with Dynamic Capacity and Co-evolutionary Genetic Algorithm

The quickly increasing of airport flow without suitable support resources is the major cause for congestions and delays. In order to take better use of airport capacity, the air traffic flow management model with dynamic capacity has been developed in this paper based on MAGHP (multi-airport ground holding problem), and a new algorithm, co-evolutionary genetic algorithm (co-GA) is proposed for this model. The airport capacity is considered to be determinate in the original model; and the airborne holding can be transformed to the ground completely. But actually the relationship between the departure and arrival capacity can be presented by curves according to different runway configurations, weather conditions and so on. Besides, airborne holding can not be avoided in certain congested airports sometimes. So the departure and arrival capacities are considered to be variables according to the demands in this paper, and in order to minimize the cost of both airborne and ground holding we assign controlled slot to the flight based on the best allocation of arrivals and departures by the co-GA. The model and co-GA was validated by practical data from Beijing, Shanghai and Guangzhou ATC Centers of the Civil Aviation Administration of China.