Airport gate assignment problem with deep reinforcement learning  

作　　者：Zhao Jiaming Wu Wenjun Liu Zhiming Han Changhao Zhang Xuanyi Zhang Yanhua 赵家明;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)

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,P.R.China [2]IT Department,Beijing Capital International Airport Co.Ltd,Beijing 100124,P.R.China

出　　处：《High Technology Letters》2020年第1期102-107,共6页高技术通讯（英文版）

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)。

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.

关 键 词：AIRPORT gate ASSIGNMENT problem(AGAP) DEEP reinforcement learning(DRL) MARKOV decision process(MDP) 

分 类 号：V351[航空宇航科学与技术—人机与环境工程] TP18[自动化与计算机技术—控制理论与控制工程]