摘要
为了将智能Agent技术架构应用于机场货运业务的仿真模型开发,以机场货运资源优化为目标,提出了将深度强化学习与机场货运业务仿真模型结合的决策支持系统框架,用仿真数据实现对深度学习网络的训练,运用深度学习网络优化模型中的调度方案。训练成熟的系统采取在线模式,可以用于实时优化货运流程的调度方案。为了验证架构的有效性,在Anylogic仿真平台进行模型开发和实验,并将深度强化学习的调度与OptQuest的优化结果进行比较。结果表明,在保证机场货运业务有序进行的基础上,深度强化学习可以更好地对机场货运业务进行优化。
An intelligent agent technology architecture is adopted to the simulation model development of airport cargo business.Aiming at the optimization of airport cargo resources,a decision support system framework combining deep reinforcement learning(DRL)and airport cargo business simulation model is proposed.The simulated results are applied as the training data of the DRL network,and the DRL is used to optimize operation parameter of the simulation model.The mature system can be run online,which can provide optimized operation order in real time.In order to verify the effectiveness of the architecture,model development and experiments are conducted in Anylogic simulation platform,and the performances of DRL and OptQuest are compared.The results show that DRL can better optimize airport cargo business on the basis of ensuring orderly airport cargo operations.
作者
王红微
杨鹏
Wang Hongwei;Yang Peng(Tianjin University of Technology,Tianjin 300384,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2022年第3期651-660,共10页
Journal of System Simulation
基金
国家自然科学基金青年科学基金(61603396)
中国民航局安全能力专项(TMSA2017-246-1/2)。
关键词
在线仿真
机场货运业务
深度强化学习
仿真模型优化
online simulation
airport cargo business
deep reinforcement learning
simulation model optimization
