Study on dynamic scheduling method of airport refueling vehicles based on DQN

Abstract

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.

Keywords

• airport refueling vehicle
• dynamic scheduling
• deep reinforcement learning
• deep q-network
• multi-objective optimization