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Using Machine Learning to Optimize Warehouse Operations

Dec 14, 2015
By Brad Nemire
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With thousands of orders placed every hour and each order assigned to a pick list, Europe’s leading online fashion retailer Zalando is using GPU-accelerated deep learning to identify the shortest route possible to products in their 1.3 million-square-foot distribution center.

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Two schematics of a rope ladder warehouse zone with picks. The blue shelves denote shelves with items to be picked, so the goal is to find the shortest possible route that allows a worker to visit all blue shelves while starting and ending at the depot.

Calvin Seward, a Data Scientist focused on warehouse logistics, shares how his team is using the Caffe deep learning framework and Tesla K80 GPUs to train their deep neural network to greatly accelerate a processing bottleneck, which in turn enabled the company to more efficiently split work between workers.
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Data Science | Robotics | Retail / Consumer Packaged Goods | News | Machine Learning & Artificial Intelligence

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About Brad Nemire
Brad Nemire leads the Developer Communications team at NVIDIA focused on evangelizing amazing GPU-accelerated applications. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University.

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