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A Multi-GPU Parallel Genetic Algorithm For Large-Scale Vehicle Routing Problems

The Vehicle Routing Problem (VRP) is fundamental to logistics operations. Finding optimal solutions for VRPs related to large, real-world operations is computationally expensive. Genetic algorithms (GA) have been used to find good solutions for different types of VRPs but are slow to converge. This work utilizes high-performance computing (HPC) platforms to design a parallel GA (PGA) algorithm for solving large-scale VRP problems. The algorithm is implemented on an eight-GPU NVIDIA DGX-l server. Maximum parallelism is achieved by mapping all algorithm arrays into block threads to achieve high throughput and reduced latency for full GPU utilization. Tests with VRP benchmark problems of up to 20,000 nodes compare the algorithm performance (speed) with different GPU counts and a multi-CPU implementation. The developed algorithm provides the following improvements over CPU or single-GPU-based algorithms: (i) larger problem sizes up to 20,000 nodes are handled, (ii) execution time is reduced over the CPU by a factor of 1,700, and iii) for the range tested, the performance increases monotonically with the number of GPUs.