Airport gate assignment problem with deep reinforcement learning

被引：0
作者：
Zhao J. [1 ]
Wu W. [1 ]
Liu Z. [1 ]
Han C. [1 ]
Zhang X. [2 ]
Zhang Y. [1 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] IT Department, Beijing Capital International Airport Co. Ltd, Beijing
来源：
High Technology Letters | 2020年 / 26卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Airport gate assignment problem (AGAP); Deep reinforcement learning (DRL); Markov decision process (MDP);
D O I：
10.3772/j.issn.1006-6748.2020.01.014
中图分类号：
学科分类号：
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. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
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收藏
页码：102 / 107
页数：5
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