  In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem,
where agents constantly engage with new tasks and need to plan collision-free
paths to execute them. To execute a task, an agent needs to visit a pair of
goal locations, consisting of a pickup location and a delivery location. We
propose two variants of an algorithm that assigns a sequence of tasks to each
agent using the anytime algorithm Large Neighborhood Search (LNS) and plans
paths using the Multi-Agent Path Finding (MAPF) algorithm Priority-Based Search
(PBS). LNS-PBS is complete for well-formed MAPD instances, a realistic subclass
of MAPD instances, and empirically more effective than the existing complete
MAPD algorithm CENTRAL. LNS-wPBS provides no completeness guarantee but is
empirically more efficient and stable than LNS-PBS. It scales to thousands of
agents and thousands of tasks in a large warehouse and is empirically more
effective than the existing scalable MAPD algorithm HBH+MLA*. LNS-PBS and
LNS-wPBS also apply to a more general variant of MAPD, namely the Multi-Goal
MAPD (MG-MAPD) problem, where tasks can have different numbers of goal
locations.
