A comparative study of meta-heuristics for the aircraft landing scheduling problem

Air traffic flow has faced a big increase in the last decade, becoming one of the main transportation solutions. Federal Aviation Administration (FAA) and aeronautical industries are both predicting a growth between 150% and 250% over the next two decades. With this increasing number of aircrafts in the air, airports need also to improve their scheduling strategies to better control the coordination of landing planes. Air Traffic Controllers (ATCO) seek to minimize the waiting time on the aircraft waiting queue. However the ATCO normally use a First Come First Served (FCFS) technique driven by its simplicity. However, it has the disadvantage of offering poor performance when opposed to other strategies. Aiming to characterize and evaluate this performance and compare it with other algorithms, we have tested the application of some scheduling meta-heuristics to this problem, namely Tabu Search (TS) and Simulated Annealing (SA). Experiments show that SA outperforms TS by up to 55% and FCFS by up to 19%. We recommend that using the SA could help managers enhance their decisions over the currently used policies (FCFS) by providing a better scheduling for landing control and saving companies' time and money.