关键词: 交通运输工程；航班延误恢复；延误损失；航班重排班；马尔科夫决策；深度强化学习

Abstract: Flight delays at airports resulted in aircraft and passengers being stranded at the airport, and improper recovery and scheduling of flight delays can exacerbate the losses caused by delays. Aiming to the issue of minimizing losses in flight delay recovery scheduling, a target function was formulated to calculate the total delay loss, a Markov decision-making process was constructed for flight delay recovery, and an airport flight delay recovery rescheduling model was established. To address computational complexity, a deep learning neural network parameterized policy function was employed to parameterize the strategy of reducing the delay loss objective function value, which was trained by the reward function and advantage function. A reinforcement learning algorithm for airport flight delay recovery was proposed. The research results show that the proposed model can reduce the total loss of flight delays by 7.83% and the duration of passenger delays by 7.23%. The proposed deep reinforcement learning algorithm outperforms other algorithms in both time and performance.

Key words: traffic and transportation engineering; flight delay recovery; delay losses; flight rescheduling; Markov decision; deep reinforcement learning