Uma meta heurística baseada em busca populacional para o problema de alocação de aeronaves à portões aeroportuários.

Abstract

The Gate Assignment Problem (GAP) in the airport context consists of optimizing the allo- cation of aircraft to the boarding and disembarkation gates, considering different objectives and operational constraints. Amid the growth of air traffic in recent years, there is a need to improve this process, with an emphasis on minimizing the displacement of passengers and baggage. In view of this scenario, this study proposes a hybrid algorithm, combining Ant Colony Optimization (ACO) and Local Search (LS), to address the allocation problem in airports with a single terminal and homogeneous gates, as well as multiple terminals with heterogeneous gates. For this, a systematic review and bibliometric analysis were carried out to identify the research trends over the years, the most used methods, the objectives frequently considered and the gaps that still exist, followed by the mathematical modeling of the problem, the construction of the algorithm and the generation of test instances based on three real Brazilian airports. Finally, the computational experiments were conducted by applying the test instances to the developed algorithm and the Gurobi solver. In the tests carried out for Fortaleza Airport (FOR), the algorithm achieved high-quality solutions in less than 1 minute, with an average Gap (%) of less than 1%. For Galeão Airport (GIG), even with greater complexity and up to 150 flights, the method maintained operational viability, returning solutions in about 1 minute. For Guarulhos Airport (GRU), the most challenging scenario, viable allocations were obtained in less than 3.5 minutes, demonstrating the quality of the proposed method in the face of complex constraints. The results confirm the effectiveness of the proposed approach to reduce internal displacements of passengers and baggage, and also highlight relevant operational and environmental benefits.