Real-time AGV scheduling optimisation method with deep reinforcement learning for energy-efficiency in the container terminal yard

作者：Lin Gong, Zi-Jie Huang, Xi Xiang, Xin Liu · 发表于：International Journal of Production Research · 年份：2024 · DOI：10.1080/00207543.2024.2325583 · 被引用次数：47 · 研究领域：Computer Science

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.