A meta-learning framework for cross-domain order scheduling in intelligent warehouses
提出一个基于元学习的仓库订单调度框架Meta-Scheduler，通过异构图表征、超网络策略生成和最优传输分配，在冷启动场景下将订单完成时间降低40.6%、负载基尼系数降低55.8%，并支持200机器人规模的实时决策。
Dynamic order scheduling in intelligent warehouse systems faces critical challenges within the domain of production research, including heterogeneous robot allocation, load balancing, and cross-scenario generalisation. This study proposes a meta-learning-based warehouse order scheduling framework (Meta-Scheduler) that adapts optimisation paradigms from cloud computing task scheduling to achieve efficient, adaptive scheduling in dynamic environments. The framework comprises three innovative components: (1) a hierarchical meta-feature extractor that encodes warehouse topology and order patterns via heterogeneous graph neural networks, (2) a dynamic hypernetwork policy generator enabling cross-scenario knowledge transfer through a differentiable neural architecture search, and (3) a robust optimal transport allocator that balances efficiency and load balancing via the entropy-regularized Sinkhorn algorithm. Experiments demonstrate that compared with traditional genetic algorithms and deep reinforcement learning, Meta-Scheduler decreases the order completion time and load Gini coefficient by 40.6% and 55.8%, respectively, in cold-start scenarios while achieving 81% faster convergence in multi-warehouse transfer tasks. Furthermore, the framework maintains real-time subsecond decision capability at the 200-robot scale, meeting industrial deployment requirements. This research establishes a scalable theoretical framework for dynamic warehouse scheduling and provides new insights for the production research community into the migration of the cross-domain task scheduling algorithm.