NUNES, P. S. A.; NUNES, Paloma dos Santos Alves.
Resumo:
Air traffic has increased significantly in recent years due to the growth in passenger flow and the demand for commercial cargo logistics. This, coupled with the rising number of operations and flights, has resulted in a considerable increase in the complexity of airport planning. In this context, this study addresses the Aircraft Landing Problem (ALP), which is an NP-Hard problem of significant relevance for efficient air traffic control. The ALP involves scheduling aircraft landing times at airports, typically aiming to minimize the total penalty cost associated with deviations between the target and assigned landing times. This scheduling must adhere to a set of real-world constraints, such as ensuring that each landing time falls within a specified time window and maintaining the required separation time between pairs of aircraft. To solve the ALP in single-runway configurations, this study proposes a heuristic algorithm based on a multi-objective evolutionary approach to minimize both the total deviation cost and the makespan, Multi-objective Particle Swarm Optimization (MOPSO). The performance of the algorithm is evaluated using a set of benchmark instances from the literature, involving up to 20 aircraft, as well as real-world instances from Recife International Airport, with up to 100 aircraft. Computational experiments demonstrated that the proposed heuristic achieved competitive results compared to other analyzed heuristics, showing better performance in 60% of the benchmark instances (with optimal solutions reached in 40% of these cases) for the single-objective approach. When considering multiple objectives, the MOPSO provided high-quality solutions within a satisfactory computational time.
