Gate Assignment Algorithm for Airport Peak Time Based on Reinforcement Learning

Chenwei Zhu, Zhenchun Wei, Zengwei Lyu, Xiaohui Yuan, Dawei Hang, Lin Feng
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Abstract

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.
基于强化学习的机场高峰时段登机口分配算法
在现有的机场登机口分配研究中,很少考虑到高峰期登机口资源有限的情况。在这种情况下,一些航班可能无法正常停靠。本文研究了高峰时段下的机场登机口分配问题。我们提出了一种登机口预分配模型,以最大限度地提高登机口匹配度和近登机口乘客分配率。此外,为了最小化预分配登机口变更率,我们在预分配模型的基础上提出了动态重新分配模型。考虑到该问题的非确定性多项式难(NP-hard)特性,我们提出了一种基于近端策略优化的登机口分配算法(GABPPO)。仿真结果表明,该算法能有效解决机场高峰期的登机口短缺问题。与自适应并行遗传算法、深度 Q 网络算法和策略梯度算法相比,本文提出的算法得到的解的目标值在近登机口乘客分配率方面分别提高了 5.7%、3.6% 和 7.9%,在登机口匹配度方面分别提高了 10.6%、4.9% 和 11.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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