  Efficiently solving path planning problems for a large number of robots is
critical to the successful operation of modern warehouses. The existing
approaches adopt classical shortest path algorithms to plan in environments
whose cells are associated with both space and time in order to avoid collision
between robots. In this work, we achieve the same goal by means of simulation
in a smaller static environment. Built upon the new framework introduced in
(Bertsekas, 2021a), we propose multiagent rollout with reshuffling algorithm,
and apply it to address the warehouse robots path planning problem. The
proposed scheme has a solid theoretical guarantee and exhibits consistent
performance in our numerical studies. Moreover, it inherits from the generic
rollout methods the ability to adapt to a changing environment by online
replanning, which we demonstrate through examples where some robots
malfunction.
