  With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have
studied how MAPF algorithms can be deployed to coordinate hundreds of robots in
large automated warehouses. While most works try to improve the throughput of
such warehouses by developing better MAPF algorithms, we focus on improving the
throughput by optimizing the warehouse layout. We show that, even with
state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead
to congestion for warehouses with large numbers of robots and thus have limited
scalability. We extend existing automatic scenario generation methods to
optimize warehouse layouts. Results show that our optimized warehouse layouts
(1) reduce traffic congestion and thus improve throughput, (2) improve the
scalability of the automated warehouses by doubling the number of robots in
some cases, and (3) are capable of generating layouts with user-specified
diversity measures. We include the source code at:
https://github.com/lunjohnzhang/warehouse_env_gen_public
