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We introduce a new problem formulation, Double-Deck Multi-Agent Pickup andDelivery (DD-MAPD), which models the multi-robot shelf rearrangement problem inautomated warehouses. DD-MAPD extends both Multi-Agent Pickup and Delivery(MAPD) and Multi-Agent Path Finding (MAPF) by allowing agents to move beneathshelves or lift and deliver a shelf to an arbitrary location, thereby changingthe warehouse layout. We show that solving DD-MAPD is NP-hard. To tackleDD-MAPD, we propose MAPF-DECOMP, an algorithmic framework that decomposes aDD-MAPD instance into a MAPF instance for coordinating shelf trajectories and asubsequent MAPD instance with task dependencies for computing paths for agents.We also present an optimization technique to improve the performance ofMAPF-DECOMP and demonstrate how to make MAPF-DECOMP complete for well-formedDD-MAPD instances, a realistic subclass of DD-MAPD instances. Our experimentalresults demonstrate the efficiency and effectiveness of MAPF-DECOMP, with theability to compute high-quality solutions for large-scale instances with overone thousand shelves and hundreds of agents in just minutes of runtime.

Double-Deck Multi-Agent Pickup and Delivery: Multi-Robot Rearrangement in Large-Scale Warehouses