Using Machine Learning to Optimize Warehouse Operations

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.

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.

Read more on Parallel Forall >>

About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter
  • Raghunandan Palakodety

    good reads.