  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.
