| Zhao Jiaming（赵家明）*,Wu Wenjun*,Liu Zhiming*,Han Changhao*,Zhang Xuanyi**,Zhang Yanhua*.[J].高技术通讯(英文),2020,26(1):102~107 |
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| Airport gate assignment problem with deep reinforcement learning |
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| DOI：doi：10.3772/j.issn.1006-6748.2020.01.014 |
| 中文关键词: |
| 英文关键词: airport gate assignment problem (AGAP), deep reinforcement learning (DRL), Markov decision process (MDP) |
| 基金项目: |
| Author Name | Affiliation | | Zhao Jiaming（赵家明）* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China) | | Wu Wenjun* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China) | | Liu Zhiming* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China) | | Han Changhao* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China) | | Zhang Xuanyi** | (**IT Department, Beijing Capital International Airport Co. Ltd, Beijing 100124, P.R.China) | | Zhang Yanhua* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| 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. |
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