Download PDFOpen PDF in browserQ-Learning for Outbound Container Stacking at Container TerminalsEasyChair Preprint 139436 pages•Date: July 12, 2024AbstractThe efficient stacking of outbound containers presents a significant challenge within container terminal operations. It's crucial to minimize the anticipated need for rehandling, as this directly impacts yard productivity and overall terminal efficiency. To address this challenge, we introduce a novel approach based on reinforcement learning. Our method employs Q-learning, incorporating Monte Carlo techniques to identify optimal storage locations by maximizing reward values. Furthermore, we've developed effective strategies for determining storage placements through extensive training iterations. Through numerical experimentation using real-world container terminal data, we've compared our model with existing algorithms. Numerical results highlight the robustness of our approach in navigating uncertain operational environments, its ability to support real-time decision-making, and its effectiveness in minimizing rehandling requirements. Keyphrases: Container stacking problem, Q-learning, container terminal, yard management
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