Metaheuristics for efficient aircraft scheduling and re-routing at busy terminal control areas

Intelligent decision support systems for the real-time management of landing and take-off operations can be very effective in helping traffic controllers to limit airport congestion at busy terminal control areas. The key optimization problem to be solved to this aim can be formulated as a mixed integer linear program. However, since this problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY solver in order to improve the possibility of finding good quality solutions quickly. The proposed framework starts from a good initial solution for the scheduling problem with fixed routes, obtained via a truncated branch-and-bound algorithm. A metaheuristic is then applied to improve the solution by re-routing some aircraft. The new metaheuristics are based on variable neighbourhood search, tabu search and hybrid schemes. The neighbourhoods differ from each other for the number of aircraft that are re-routed in each move. Computational experiments are performed on an Italian terminal control area under various types of disturbances, including multiple aircraft delays and a temporarily disrupted runway. The metaheuristics achieve solutions of remarkable quality, within a small computation time, compared with a commercial solver and with the previous versions of AGLIBRARY.