Airport gate assignment problem with deep reinforcement learning

查看全文 作　　者：[1]赵家明;Wu [1]Wenjun;Liu [1]Zhiming;Han [1]Changhao;Zhang [2]Xuanyi;Zhang [1]Yanhua

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》索引2020年第26卷第1期,共6页

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)

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Airport gate assignment problem with deep reinforcement learning

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