A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915986023645184 |
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| author | Lei, Zhenyu Hao, Jin-Kao |
| author_facet | Lei, Zhenyu Hao, Jin-Kao |
| contents | Multi-depot vehicle routing problems (MDVRPs) are prevalent in a variety of practical applications. However, they are computationally challenging to solve due to their inherent complexity. This paper proposes an effective hybrid algorithm for a class of MDVRPs. The algorithm integrates a learning-driven, diversity-controlled route-exchange crossover and a multi-depot-supported feasible-and-infeasible search framework guided by a multi-penalty evaluation function. Two dedicated depot-related local search operators are incorporated to further strengthen the search capability in multi-depot settings. To improve computational efficiency and scalability, an enhanced version of the algorithm is developed that uses a tensor-based GPU acceleration combined with a novel multi-move update strategy. Extensive computational experiments on benchmark instances of three MDVRP variants show that the proposed algorithms are highly competitive with state-of-the-art methods, especially for large-scale instances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05208 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems Lei, Zhenyu Hao, Jin-Kao Robotics Distributed, Parallel, and Cluster Computing Optimization and Control Multi-depot vehicle routing problems (MDVRPs) are prevalent in a variety of practical applications. However, they are computationally challenging to solve due to their inherent complexity. This paper proposes an effective hybrid algorithm for a class of MDVRPs. The algorithm integrates a learning-driven, diversity-controlled route-exchange crossover and a multi-depot-supported feasible-and-infeasible search framework guided by a multi-penalty evaluation function. Two dedicated depot-related local search operators are incorporated to further strengthen the search capability in multi-depot settings. To improve computational efficiency and scalability, an enhanced version of the algorithm is developed that uses a tensor-based GPU acceleration combined with a novel multi-move update strategy. Extensive computational experiments on benchmark instances of three MDVRP variants show that the proposed algorithms are highly competitive with state-of-the-art methods, especially for large-scale instances. |
| title | A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems |
| topic | Robotics Distributed, Parallel, and Cluster Computing Optimization and Control |
| url | https://arxiv.org/abs/2605.05208 |