Gate Assignment Algorithm for Airport Peak Time Based on Reinforcement Learning

In existing airport gate allocation studies, little consideration has been given to situations where gate resources are limited during peak periods. Under such circumstances, some flights may not be able to make regular stops. In this paper, the airport gate assignment problem under peak time is investigated. We propose a gate pre-assignment model to maximize the gate matching degree and the near gate passenger allocation rate. Besides, to minimize the pre-assignment gate change rate, we propose a dynamic reassignment model based on the pre-assignment model. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a gate assignment algorithm based on proximal policy optimization (GABPPO) is proposed. The simulation results show that the algorithm can effectively solve the gate shortage problem during the airport peak period. Compared with the adaptive parallel genetic, deep Q-network, and policy gradient algorithms, the target value of solutions obtained by the proposed algorithm in the near gate passenger allocation rate is increased by 5.7%, 3.6%, and 7.9%, respectively, and the target value in the gate matching degree is increased by 10.6%, 4.9%, and 11.5% respectively.