Deep reinforcement learning approach for real-time airport gate assignment

Assigning aircraft to gates is one of the most important daily decision problems that airport professionals face. The solution to this problem has raised a significant effort, with many researchers tackling many different variants of this problem. However, most existing studies on gate assignment contain only a static perspective without considering possible future disruptions and uncertainties. We bridge this gap by looking at gate assignments as a dynamic decision-making process. This paper presents the Real-time Gate Assignment Problem Solution (REGAPS) algorithm, an innovative method adept at resolving pre-assignment issues and dynamically optimizing gate assignments in real-time at airports through the integration of Deep Reinforcement Learning (DRL). This work represents the first time that DRL is used with real airport data and a configuration containing a large number of flights and gates. The methodology combines a tailored Markov Decision Process (MDP) formulation with the Asynchronous Advantage Actor-Critic (A3C) architecture. Multiple factors, such as flight schedules, gate availability, and passenger walking time, are considered. An empirical case study demonstrates that the REGAPS outperforms two classic deep Q-learning algorithms and a traditional Genetic Algorithm in terms of reducing passenger walking time and apron gate assignment. Finally, supplementary experiments highlight REGAPS's adaptability under various gate assignment rules for international and domestic flights. The finding demonstrates that not only did REGAPS outperform COVID restrictions, but it can also produce considerable benefits under other policies.