Hyper-heuristics Framework for Online Order Batching and Sequencing Problem in Automated Logistics Warehouses with Order Consolidation Process

This paper presents a hyper-heuristics framework for the Online Order Batching and Sequencing Problem (OOBSP) in automated logistics warehouses that receive orders in real time and are equipped with Goods-to-Person (GtP) and Person-to-Goods (PtG) picking systems, followed by order consolidation. Two novel heuristics are introduced to capture the dynamics of the order consolidation process that significantly affect order completion time. These heuristics are combined with five conventional ones in a unified hyper-heuristics framework, where the weight coefficients of candidate heuristics are automatically adjusted to minimize makespan and total shipment delay. To determine these coefficients, two schemes are investigated: a static hyper-heuristics, in which fixed weights are optimized offline, and an adaptive hyper-heuristics, in which weights are determined online by a policy trained via reinforcement learning. Discrete simulation experiments using realistic order streams show that adaptive hyper-heuristics reduce makespan by 2.49% and shipment delay by 82.93% compared to a First-Come, First-Served baseline. Furthermore, the analysis demonstrates the effectiveness of the two proposed heuristics and confirms that the adaptive scheme outperforms the static scheme by dynamically tuning weights according to work progress and line status.