  Multi-Agent Path Finding (MAPF) is the problem of finding a set of
collision-free paths for multiple agents in a shared environment while
minimizing the sum of travel time. Since solving the MAPF problem optimally is
NP-hard, anytime algorithms based on Large Neighborhood Search (LNS) are
promising to find good-quality solutions in a scalable way by iteratively
destroying and repairing the paths. We propose Destroy-Repair Operation
Parallelism for LNS (DROP-LNS), a parallel framework that performs multiple
destroy and repair operations concurrently to explore more regions of the
search space within a limited time budget. Unlike classic MAPF approaches,
DROP-LNS can exploit parallelized hardware to improve the solution quality. We
also formulate two variants of parallelism and conduct experimental
evaluations. The results show that DROP-LNS significantly outperforms the
state-of-the-art and the variants.
