A storage assignment approach considering SKU repetitions in sequential zone picking

Sequential zone picking systems often suffer from inefficiencies when high-demand variability coincides with a small set of distinct stock-keeping units (SKUs). Introducing SKU repetitions – placing identical SKUs in multiple zones – can mitigate imbalances between zones, but poses a complex storage-assignment challenge. When an order includes a SKU stored in multiple zones, a decision must be made regarding the optimal zone from which to pick it. The storage assignment problem with SKU repetitions is modeled as a binary program. Since the model formulation is NP-hard, a three-stage heuristic storage assignment approach is proposed, that explicitly anticipates downstream order-routing decisions. First, a decision is made how often each SKU is repeated. Then SKUs are grouped based on their co-occurrence in the order file. A variable neighborhood search iteratively relocates SKUs across zones to balance pick-face density and inter-zone travel times. Performance is assessed via a simulation calibrated with real-world HelloFresh order and facility data . The proposed heuristic outperforms benchmark approaches on average by up to 19.5%. Besides, the simulation study shows that minor number of SKU repetitions can significantly improve overall ZPS performance. Finally, the algorithm is tested in a real-world ZPS showing up to 40% efficient gains in zone workload balancing. Anticipatory, storage assignment considering SKU repetitions significantly enhances ZPS efficiency. The proposed heuristic is computationally tractable and practically implementable, offering logistics managers clear guidelines for setting SKU repetition levels. • Pioneering study generalizes zone picking by permitting duplicates across zones. • Heuristic framework beats benchmarks by up to 10%, yielding operational gains. • Computational studies with real-world data affirm the approach’s practical relevance. • SKU repetitions boost zone picking robustness to order changes and stock shortages. • Real-world use confirms the solution’s practicality and effectiveness.