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- 1. How Zalando Accelerates Warehouse Operations with Neural Networks Calvin Seward Big Data Berlin v. 6.0 28 January 2016 1 / 22
- 2. Outline Picker Routing Problem Order Batching Problem Neural Network Estimate of Pick Route Length Order Batch optimization via Simulated Annealing 2 / 22
- 3. Outline Picker Routing Problem Order Batching Problem Neural Network Estimate of Pick Route Length Order Batch optimization via Simulated Annealing This was a project that was a collaboration between Rolland Vollgraf, Sebastian Heinz and myself. Some of the gures have been shamelessly stolen from Roland. 2 / 22
- 4. Zalando's Logistics Centers 13.8 Million orders from 01.07.15 30.09.15 174,684 orders send per working day (Mo. Sa.) Every second handling time per order requires 160 man-days of work / month Any increase in ecency has a big impact 3 / 22
- 5. Picker Routing Problem The locations that must be visited 4 / 22
- 6. Picker Routing Problem The locations that must be visited 5 / 22
- 7. Picker Routing Problem The Pick Route 6 / 22
- 8. Picker Routing Problem The Pick Route 7 / 22
- 9. Picker Routing Problem The Pick Route 8 / 22
- 10. Picker Routing Problem The Pick Route 9 / 22
- 11. Picker Routing Problem The Pick Route 10 / 22
- 12. Picker Routing Problem The Pick Route 11 / 22
- 13. OCaPi Algorithm Optimal CArt PIck To solve picker routing problem, we developed the OCaPi Algorithm Calculates the optimal route to walk Also determines optimal cart handling strategy Figure: The Okapi Our Mascot 12 / 22
- 14. OCaPi Algorithm Optimal CArt PIck To solve picker routing problem, we developed the OCaPi Algorithm Calculates the optimal route to walk Also determines optimal cart handling strategy Has complexity that is linear in the number of aisles Figure: The Okapi Our Mascot 12 / 22
- 15. OCaPi Algorithm Optimal CArt PIck To solve picker routing problem, we developed the OCaPi Algorithm Calculates the optimal route to walk Also determines optimal cart handling strategy Has complexity that is linear in the number of aisles Unfortunately still has a runtime of around 1 second Figure: The Okapi Our Mascot 12 / 22
- 16. Simplied Order Batching Problem Bipartite Graph Formulation n Orders o1 o2 o3 . . . on m Pick Tours t1 t2 . . . tm
- 17. Simplied Order Batching Problem Bipartite Graph Formulation n Orders o1 o2 o3 . . . on m Pick Tours t1 t2 . . . tm
- 18. Simplied Order Batching Problem Bipartite Graph Formulation n Orders o1 o2 o3 . . . on m Pick Tours t1 t2 . . . tm 13 / 22
- 19. Order Batching Problem Random Split of 10 Orders à 2 Items Into Two Pick Routes 14 / 22
- 20. Order Batching Problem Brute Force Split of 10 Orders à 2 Items into Optimal Two Pick Routes → 8.3% Boost 15 / 22
- 21. Neural Network Estimate of Pick Route Length This simple example could be done with brute force A realistic example with 40 orders à 2 items has a complexity of 40! 2 · 20! · 20! ≈ 6.9 · 1010 at 1 second per route, you'd wait 2185 years 16 / 22
- 22. Neural Network Estimate of Pick Route Length This simple example could be done with brute force A realistic example with 40 orders à 2 items has a complexity of 40! 2 · 20! · 20! ≈ 6.9 · 1010 at 1 second per route, you'd wait 2185 years Use clever heuristics like simulated annealing Estimate pick route length with Neural Networks 16 / 22
- 23. Neural Network Estimate of Pick Route Length OCaPi cost landscape f : (N × R)n → R+ is a nice function because it is: Lipschitz continuous in the real-valued arguments Piecewise linear in the real-valued arguments Locally sensitive 17 / 22
- 24. Neural Network Estimate of Pick Route Length OCaPi cost landscape f : (N × R)n → R+ is a nice function because it is: Lipschitz continuous in the real-valued arguments Piecewise linear in the real-valued arguments Locally sensitive Perfect function to model with Convolutional Neural Networks with ReLUs: ˜f(x) := (W2(W1x + b1)+ + b2)+ Train convolutional neural network with 1 million examples 17 / 22
- 25. Neural Network Estimate of Pick Route Length Estimation Accuracy Frequency of relative estimation error estimated travel time calculated travel time 18 / 22
- 26. Neural Network Estimate of Pick Route Length Estimation Speed Time Per Route on two Intel Xeon E5-2640 and two NVIDIA Tesla K80 accelerators number pick lists OCaPi CPU network GPU network 1 5.369 2.202e-3 1.656e-3 10 1.326 1.991e-4 1.832e-4 100 0.365 6.548e-5 5.919e-5 1000 3.086e-5 2.961e-5 10000 2.554e-5 2.336e-5 19 / 22
- 27. Order Batch optimization via Simulated Annealing Estimated and Exact Improvement in Example Simulated Annealing Run 20 / 22
- 28. Questions? 21 / 22
- 29. Thanks for listening Get The Slides On github.com/cseward/ocapi_neural_net_blog_post 22 / 22

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