摘要
With the rapid development of air transportation in recent years,airport operations have attracted a lot of attention.Among them,airport gate assignment problem(AGAP)has become a research hotspot.However,the real-time AGAP algorithm is still an open issue.In this study,a deep reinforcement learning based AGAP(DRL-AGAP)is proposed.The optimization object is to maximize the rate of flights assigned to fixed gates.The real-time AGAP is modeled as a Markov decision process(MDP).The state space,action space,value and rewards have been defined.The DRL-AGAP algorithm is evaluated via simulation and it is compared with the flight pre-assignment results of the optimization software Gurobiand Greedy.Simulation results show that the performance of the proposed DRL-AGAP algorithm is close to that of pre-assignment obtained by the Gurobi optimization solver.Meanwhile,the real-time assignment ability is ensured by the proposed DRL-AGAP algorithm due to the dynamic modeling and lower complexity.
作者
赵家明
Wu Wenjun
Liu Zhiming
Han Changhao
Zhang Xuanyi
Zhang Yanhua
Zhao Jiaming;Wu Wenjun;Liu Zhiming;Han Changhao;Zhang Xuanyi;Zhang Yanhua(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,P.R.China;IT Department,Beijing Capital International Airport Co.Ltd,Beijing 100124,P.R.China)
基金
Supported by the National Natural Science Foundation of China(No.U1633115)
the Science and Technology Foundation of Beijing Municipal Commission of Education(No.KM201810005027)。