Order picking is a crucial operation in warehouses that significantly impacts overall efficiency and profitability. This study addresses the dynamic order picking problem, a significant concern in modern warehouse management where real-time adaptation to fluctuating order arrivals and efficient picker routing are crucial. Traditional methods, often assuming fixed order sets, fall short in this dynamic environment. We utilize Deep Reinforcement Learning (DRL) as a solution methodology to handle the inherent uncertainties in customer demands. We focus on a single-block warehouse with an autonomous picking device, eliminating human behavioral factors. Our DRL framework enables the dynamic optimization of picker routes, significantly reducing order throughput times, especially under high order arrival rates. Experiments demonstrate a substantial decrease in order throughput time and unfulfilled orders compared to benchmark algorithms. We further investigate integrating a hyperparameter in the reward function that allows for flexible balancing between distance traveled and order completion time. Finally, we demonstrate the robustness of our DRL model for out-of-sample test instances.

本研究解决了现代仓库管理中的动态订单拣选问题，传统方法在面对订单流动性时存在不足。通过采用深度强化学习（DRL），我们优化了拣选设备的路径，显著减少了订单处理时间，尤其在高订单到达率情况下。实验结果显示，我们的方法在减少未完成订单和提高效率方面具有显著优势。

仓库运营中的动态订单拣选深度强化学习