A reinforcement learning approach for multi-fleet aircraft recovery under airline disruption

An airline scheduler plans flight schedules with efficient resource utilization. However, unpredictable airline disruptions, such as temporary closures of an airports, cause schedule perturbations. Therefore, recovering disrupted flight schedules is essential for airlines. Many previous studies have relied on copies of flight arcs, which could affect the quality of solutions, and have not addressed the key measure of airlines’ on-time performance as their objective. To fill these research gaps, we propose Q-learning and Double Q-learning algorithms using the reinforcement learning approach for aircraft recovery to support airline operations. We present an artificial environment of daily flight schedules and the Markov decision process for aircraft recovery. The proposed approach is first compared with existing algorithms on the benchmark instance. In comparison with other algorithms, the developed Q-learning and Double Q-learning algorithms obtain high-quality solutions within the proper computation time. To verify that the proposed approach can be applicable to a real-world case and can adapt to realistic conditions, we employ a domestic flight schedule from one of the airlines in South Korea. We evaluate the reinforcement learning approach on a set of experiments carried out on real-world data. Computational experiments show that reinforcement learning algorithms recover disrupted flight schedules effectively, and that our approaches flexibly adapt to various objectives and realistic conditions.