Duration-of-Stay Storage Assignment under Uncertainty

Optimizing storage assignment is a central problem in warehousing. Past literature has shown the superiority of the Duration-of-Stay (DoS) method in assigning pallets, but the methodology requires perfect prior knowledge of DoS for each pallet, which is unknown and uncertain under realistic conditions. The dynamic nature of a warehouse further complicates the validity of synthetic data testing that is often conducted for algorithms. In this paper, in collaboration with a large cold storage company, we release the first publicly available set of warehousing records to facilitate research into this central problem. We introduce a new framework for storage assignment that accounts for uncertainty in warehouses. Then, by utilizing a combination of convolutional and recurrent neural network models, ParallelNet, we show that it is able to predict future shipments well: it achieves up to 29% decrease in MAPE compared to CNN-LSTM on unseen future shipments, and suffers less performance decay over time. The framework is then integrated into a first-of-its-kind Storage Assignment system, which is being piloted in warehouses across the country, with initial results showing up to 19% in labor savings.