Container port truck dispatching optimization using Real2Sim based deep reinforcement learning
针对集装箱港口卡车调度中的不确定性和动态性，提出一种结合Real2Sim仿真与空间注意力深度强化学习的系统，利用历史与实时数据学习高质量调度策略，在真实港口数据上取得最优效果。
In marine container terminals, truck dispatching optimization is often considered as the primary focus as it provides crucial synergy between the sea-side operations and yard-side activities and hence can greatly affect the terminal throughput and quay crane utilization. However, many existing studies rely on strong assumptions that often overlook the uncertainties and dynamics innate to real-life applications. In this work, we propose a dynamic truck dispatching system for container ports equipped with the latest IoT technologies. The system is comprised of Real2Sim simulation and a truck dispatch agent, trained through a spatial-attention based deep reinforcement learning module, supported by an expert network. The proposed Real2Sim framework has the ability to model the non-linear complexities and non-deterministic events while our attention-aware deep reinforcement learning module is capable of making full use of both historical and real-time port data to learn a high-quality truck dispatching policy under uncertainties. Extensive experiments show our proposed method has good generalization and achieves the state-of-the-art results on the problems derived from real-life data of a large international port.