A GPU-Accelerated Hybrid Method for a Class of Multi-Depot Vehicle Routing Problems

TL;DR

This paper introduces a GPU-accelerated hybrid algorithm for multi-depot vehicle routing problems, combining learning-driven crossover, local search, and multi-move updates to enhance efficiency and scalability.

Contribution

It presents a novel hybrid algorithm with GPU acceleration and specialized local search operators tailored for multi-depot vehicle routing problems.

Findings

The GPU-accelerated algorithm significantly reduces computation time.

The method achieves competitive or superior solutions on benchmark instances.

Enhanced scalability enables solving large-scale MDVRPs effectively.

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.