Application of Duration-of-Stay Storage Assignment with Deep Neural Networks under Uncertainty
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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. In this paper, we introduce a new framework that accounts for such uncertainty using a novel combination of convolutional and recurrent neural network models, ParallelNet. Through collaboration with a large cold storage company, we demonstrate ParallelNet achieves a 10 MAPE compared to CNN-LSTM on unseen future shipments, and suffers less performance decay as time increases. The framework is then integrated into a first-of-its-kind Storage Assignment system, which is being piloted in warehouses across the country.
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