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arXiv · 2402.01961

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

Shao-Hung Chan·Zhe Chen·Dian-Lun Lin·Yue Zhang·Daniel Harabor·Tsung-Wei Huang·Sven Koenig·Thomy Phan
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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.

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Shao-Hung Chan, Zhe Chen, Dian-Lun Lin, Yue Zhang, Daniel Harabor, Tsung-Wei Huang, Sven Koenig, Thomy Phan. 2024-02-02. Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search. https://arxiv.org/abs/2402.01961

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