logistics_ops · workflow
Zalando trains a convolutional neural network to accelerate warehouse order batching
Zalando's OCaPi algorithm produced accurate optimal picker routes but took several seconds per pick list, making it too slow to use in real-time for batching the thousands of orders placed every hour.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Order intake trigger
Thousands of new orders placed every hour must be assigned to pick lists for warehouse collection.
Tools used
CaffecuDNN_v2OpenBLASNVIDIA Tesla K80
Outcome
A convolutional neural network trained as a fast OCaPi surrogate achieved 0.895% average estimation error, enabling simulated annealing to optimize order batching and reduce worker travel time by 11% versus random batching in an experimental setting.
What failed first
Directly combining OCaPi with simulated annealing to find near-optimal pick list splits was computationally infeasible because OCaPi required several seconds per pick list evaluation, which is too slow across thousands of pick lists.
Results
Time saved32.25 seconds per hour
Volume0.895%
Grounding & classification
Source type: technical build writeup
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