  Order picking is the single most cost-intensive activity in picker-to-parts
warehouses, and as such has garnered large interest from the scientific
community which led to multiple problem formulations and a plethora of
algorithms published. Unfortunately, most of them are not applicable at the
scale of really large warehouses like those operated by Zalando, a leading
European online fashion retailer. Based on our experience in operating
Zalando's batching system, we propose a novel batching problem formulation for
mixed-shelves, large scale warehouses with zoning. It brings the selection of
orders to be batched into the scope of the problem, making it more realistic
while at the same time increasing the optimization potential. We present two
baseline algorithms and compare them on a set of generated instances. Our
results show that first, even a basic greedy algorithm requires significant
runtime to solve real-world instances and second, including order selection in
the studied problem shows large potential for improved solution quality.
