Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search

TL;DR

This paper introduces DROP-LNS, a parallel Large Neighborhood Search framework for Multi-Agent Path Finding that leverages operation parallelism to improve solution quality efficiently on parallel hardware.

Contribution

It proposes a novel parallel destroy-repair framework for MAPF, enabling concurrent operations to enhance scalability and solution quality.

Findings

DROP-LNS outperforms state-of-the-art MAPF algorithms.

Parallelism significantly improves solution quality within limited time.

Experimental results validate the effectiveness of the proposed approach.

Abstract

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.