Deep reinforcement learning for solving airport slot allocation problems

The continuous growth in aviation traffic demand has made airport slot allocation increasingly challenging. Given the complexity of the problem, reinforcement learning methods have gradually emerged as an effective approach to solving it. This paper proposes a deep reinforcement learning method to address the airport slot allocation problem, utilizing a Sequence-to-Sequence structure policy network for training to learn slot allocation strategies. We first formulate the airport slot allocation problem as a Markov decision process using a decomposition method along the time axis. Then, we design an encoder to efficiently capture state information and employ a heuristic masking generation method in the decoder to ensure the feasibility of the solution to the airport slot allocation problem, thereby providing high-quality solutions. Evaluations on real-world airport slot allocation problems show that our DRL method performs well compared to traditional heuristics and other DRL methods. Additionally, the trained policy network exhibits good generalization capabilities across instances of different sizes.