Reinforcement learning for strategic airport slot scheduling: Analysis of state observations and reward designs

• Main Authors: Nguyen-Duy, Anh, Pham, Duc-Thinh, Lye, Jian-Yi, TA, Nguyen Binh Duong
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2024
• 主題: airport slot scheduling; Reinforcement Learning; strategic; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering; Transportation
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/9268; https://ink.library.smu.edu.sg/context/sis_research/article/10268/viewcontent/RL_AirportSlot_CAI_2024_av.pdf
• 機構: Singapore Management University

Due to the NP-hard nature, the strategic airport slot scheduling problem is calling for exploring sub-optimal approaches, such as heuristics and learning-based approaches. Moreover, the continuous increase in air traffic demand requires approaches that can work well in new scenarios. While heuristics rely on a fixed set of rules, which limits the ability to explore new solutions, Reinforcement Learning offers a versatile framework to automate the search and generalize to unseen scenarios. Finding a suitable state observation and reward structure design is essential in using Reinforcement Learning. In this paper, we investigate the impact of providing the Reinforcement Learning agent with an intermediate positive signal in the reward structure along with the use of the Full State Observation and the Local State Observation. We perform training with different combinations of the reward structure, the state observation, and the Deep Q-Network (DQN) algorithm to define the training efficient formulation. We use two types of scenarios, medium and high-density, to test the ability to generalize to unseen data of the approach. Each type of scenario is used to train two separate models, Model 1 and Model 2. Model 1, which is trained on high-density scenarios, will be tested with medium-density scenarios; the results obtained will then be compared with the results of Model 2, and vice versa. We additionally analyze the performance of the DQN models with the Proximal Policy Optimization (PPO) models. Results suggest that combining the Local State Observation and the intermediate positive signal leads to a stable convergence. The obtained DQN models perform better compared to the PPO models, achieving an average displacement per request of 1.44/1.99 while only having on average 0.00/0.02 unaccommodated requests for medium/high-density scenarios. The t-statistic of 0.0810/-1.0016 and the p-value of 0.9356/0.3190 also suggest that the DQN models can generalize to unseen scenarios.