Issue |
JNWPU
Volume 42, Number 4, August 2024
|
|
---|---|---|
Page(s) | 764 - 773 | |
DOI | https://doi.org/10.1051/jnwpu/20244240764 | |
Published online | 08 October 2024 |
Study on dynamic scheduling method of airport refueling vehicles based on DQN
基于DQN的机场加油车动态调度方法研究
School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Received:
31
July
2023
Aiming at the low utilization rate of airport refueling vehicles and long solution time of exact algorithm caused by the uncertainty of actual flight time, a deep Q network dynamic scheduling method for refueling vehicles combining with the multi-objective deep reinforcement learning framework was proposed. Firstly, an optimization model is established to maximize the on-time rate of refueling tasks and the average proportion of idle vehicles. Then, the five state features that measure the current state of the vehicle are designed as inputs to the network. According to the two objectives, the two scheduling strategies are proposed as the action space so that the algorithm can generate the dynamic scheduling scheme based on the dynamic flight data in real time. Finally, the dynamic scheduling model for airport refueling vehicles is solved, and the effectiveness and real-time performance of the algorithm are verified by different scale examples. The results show that the average number of on-time refueling tasks per day is 9.43 more than that via manual scheduling, and the average working time of vehicles is reduced by 57.6 minutes, which shows the excellent ability of the present method in solving the dynamic scheduling problem of refueling vehicles.
摘要
针对实际航班时刻不确定导致机场加油车利用率低、调度实时性差的问题, 提出一种结合了多目标深度强化学习框架的深度Q网络加油车动态调度方法。建立了以最大化加油任务准时率以及平均空闲车辆占比为目标的优化模型; 设计了5个衡量车辆当前状态的状态特征作为网络的输入, 再根据2种目标提出了2种调度策略作为动作空间, 使得算法能够根据航班动态数据实时生成动态调度方案; 完成了对机场加油车动态调度模型的求解, 并利用不同规模的算例验证了算法的有效性以及实时性。将所提方法应用于实际调度中, 结果表明, 与人工调度相比, 平均每天加油任务准时完成数增加9.43个, 车辆平均工作时间减少57.6 min, DQN的结果更具优势, 提升了加油车运行效率。
Key words: airport refueling vehicle / dynamic scheduling / deep reinforcement learning / deep q-network / multi-objective optimization
关键字 : 机场加油车 / 动态调度 / 深度强化学习 / 深度Q网络 / 多目标优化
© 2024 Journal of Northwestern Polytechnical University. All rights reserved.
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