[2605.05208] A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems
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Computer Science > Robotics
arXiv:2605.05208 (cs)
[Submitted on 24 Feb 2026]
Title:A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems
Authors:Zhenyu Lei, Jin-Kao Hao
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Abstract: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.
Subjects:
Robotics (cs.RO); Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC)
Cite as: arXiv:2605.05208 [cs.RO]
(or arXiv:2605.05208v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.05208
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arXiv-issued DOI via DataCite
Submission history
From: Jin-Kao Hao [view email] [v1] Tue, 24 Feb 2026 14:15:28 UTC (681 KB)
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