A space-time network model based on improved genetic algorithm for airport taxiing scheduling problems

Abstract In order to reduce the flight delays and ease the airport congestion, a space-time network taxi scheduling model integrates the three types of conflicts was used. In the model, the aircraft taxiing schedule problem was transformed to multi-commodity network model, and the Genetic-annealing algorithm was designed to solve the problem. The simulation case showed that optimized schedule results significantly reduced the total taxiing time by 586 seconds of 17 flights compared with FCFS strategy and avoided the potential flight conflicts, which greatly improved airport operational efficiency. In addition, genetic-annealing algorithm weight out the standard genetic algorithm in convergence rate and solution efficiency.