【摘 要】
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With the rapid development of air transportation in recent years, airport operations have attrac-ted a lot of attention. Among them, airport gate assignment pro
【机 构】
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Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China;IT
论文部分内容阅读
With the rapid development of air transportation in recent years, airport operations have attrac-ted 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 rein-forcement leing 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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