The Vehicle Routing Problem (VRP) is an NP-hard scheduling problem for goods transportation with vehicle capacity and transportation cost constraints. This paper presents GPU-based parallel strategies for the Local Search Heuristic (LSH) algorithm to solve the large-scale Capacitated Vehicle Routing Problem (CVRP) instances. This work employs a combination of five improvement heuristic approaches to improve the constructed feasible solution. Typically, 99% of CPU execution time is spent in the feasible solution improvement phase. Two GPU-based parallel strategies, namely route level and customer level parallel designs, have been developed to reduce the execution time of the solution improvement phase. The proposed parallel LSH version has been tested on large-scale instances with up to 30,000 customers. When compared to the corresponding sequential version, the customer level parallel design offers a speedup of up to 147.19 times.

车辆路线问题 (VRP) 是具有车辆容量和运输成本约束的货物运输的 NP 难调度问题。本文提出了基于 GPU 的局部搜索启发式 (LSH) 算法的并行策略，以解决大规模容量车辆路由问题 (CVRP) 实例。这项工作结合了五种改进启发式方法来改进构建的可行解决方案。通常，99% 的 CPU 执行时间都花在了可行解改进阶段。已经开发了两种基于 GPU 的并行策略，即路由级和客户级并行设计，以减少解决方案改进阶段的执行时间。提议的并行 LSH 版本已经在拥有多达 30,000 个客户的大规模实例上进行了测试。