In warehousing systems, to enhance logistical efficiency amid surging demand volumes, much focus is placed on how to reasonably allocate tasks to robots. However, the robots labor is still inevitably wasted to some extent. In response to this, we propose a pre-scheduling enhanced warehousing framework that predicts task flow and acts in advance. It consists of task flow prediction and hybrid tasks allocation. For task prediction, we notice that it is possible to provide a spatio-temporal representation of task flow, so we introduce a periodicity-decoupled mechanism tailored for the generation patterns of aggregated orders, and then further extract spatial features of task distribution with novel combination of graph structures. In hybrid tasks allocation, we consider the known tasks and predicted future tasks simultaneously and optimize the allocation dynamically. In addition, we consider factors such as predicted task uncertainty and sector-level efficiency evaluation in warehousing to realize more balanced and rational allocations. We validate our task prediction model across actual datasets derived from real factories, achieving SOTA performance. Furthermore, we implement our compelte scheduling system in a real-world robotic warehouse for months of lifelong validation, demonstrating large improvements in key metrics of warehousing, such as empty running rate, by more than 50%.

翻译：在仓储系统中，为应对激增的需求量以提升物流效率，现有研究多聚焦于如何合理地将任务分配给机器人。然而，机器人的劳动力仍在一定程度上不可避免地存在浪费。针对此问题，我们提出了一种预调度增强的仓储框架，该框架能够预测任务流并提前行动。它由任务流预测与混合任务分配两部分构成。在任务预测方面，我们注意到为任务流提供时空表征是可行的，因此我们引入了一种针对聚合订单生成模式定制的周期性解耦机制，进而通过新颖的图结构组合进一步提取任务分布的空间特征。在混合任务分配中，我们同时考虑已知任务与预测的未来任务，并动态优化分配方案。此外，我们还考虑了预测任务的不确定性、仓储中扇区级效率评估等因素，以实现更均衡、更合理的分配。我们在源自真实工厂的实际数据集上验证了我们的任务预测模型，取得了SOTA性能。进一步地，我们在一个真实的机器人仓库中部署了完整的调度系统，进行了长达数月的长期验证，结果表明仓储关键指标（如空驶率）获得了超过50%的大幅提升。