Real-time AGV scheduling optimisation method with deep reinforcement learning for energy-efficiency in the container terminal yard
针对自动化集装箱码头堆场中大量自动导引车的调度难题，提出一种混合多车调度算法，通过深度强化学习实现实时决策，在仿真中降低了能耗并缩短了总完工时间。
The increasing vessel size and automation level have shifted the productivity bottleneck of automated container terminals from the terminal side to the yard side. Operating an automated container terminal (ACT) yard with a big number of automated guided vehicles (AGV) is challenging due to the complexity and dynamics of the system, severely affecting the operational efficiency and energy use efficiency. In this paper, a hybrid multi-AGV scheduling algorithm is proposed to minimise the energy consumption and the total makespan of AGVs in an ACT yard. This framework first models the AGV scheduling process as a Markov decision process (MDP). Furthermore, a novel scheduling algorithm called MDAS is proposed based on multi-agent deep deterministic policy gradient (MADDPG) to facilitate online real-time scheduling decision-making. Finally, simulation experiments show that the proposed method can effectively enhance the operational efficiency and energy use performance of AGVs in ACT yards of various scales by comparing with benchmarking methods.