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Optimizing storage assignment is a central problem in warehousing. Pastliterature has shown the superiority of the Duration-of-Stay (DoS) method inassigning pallets, but the methodology requires perfect prior knowledge of DoSfor each pallet, which is unknown and uncertain under realistic conditions. Thedynamic nature of a warehouse further complicates the validity of syntheticdata testing that is often conducted for algorithms. In this paper, incollaboration with a large cold storage company, we release the first publiclyavailable set of warehousing records to facilitate research into this centralproblem. We introduce a new framework for storage assignment that accounts foruncertainty in warehouses. Then, by utilizing a combination of convolutionaland recurrent neural network models, ParallelNet, we show that it is able topredict future shipments well: it achieves up to 29% decrease in MAPE comparedto CNN-LSTM on unseen future shipments, and suffers less performance decay overtime. The framework is then integrated into a first-of-its-kind StorageAssignment system, which is being piloted in warehouses across the country,with initial results showing up to 19% in labor savings.

Duration-of-Stay Storage Assignment under Uncertainty