Diffusion enhanced gflownet improves vehicle routing solutions
DEG: Diffusion-Enhanced GFlowNet for Solving Vehicle Routing Problems
Artificial Intelligence
Summary
Vehicle routing problems involve figuring out the best routes for vehicles to take, which can be very complex especially when there are many locations. Traditional AI approaches often fail to find a diverse set of good routes. The authors propose a new method called Diffusion-Enhanced GFlowNet (DEG) that combines two AI techniques, which helps explore better routes more effectively. They tested their method on large problem sets and found it consistently delivers better results than existing solutions.
What this means in practice
- •For logistics planners: Generate multiple high-quality vehicle routes for complex delivery networks to optimize operations and reduce costs.
- •For fleet management teams: Improve routing strategies in large-scale vehicle fleets by using richer exploration algorithms to handle tens of thousands of stops.
Authors
Ni Zhang, Zhiqin Zhang, Ling Pan, Hoong Chuin Lau, Zhiguang Cao
Abstract
Traditional neural solvers for solving vehicle routing problems (VRPs) often suffer from limited solution diversity, motivating the development of Generative Flow Network (GFlowNet)-based models. However, the effectiveness of these models is frequently constrained by insufficient flow expansion in high-reward regions, limiting their ability to distribute probability across promising solution routes. Deeper exploration could yield superior results. Diffusion models, in contrast, provide stronger structural guidance for exploration. These two paradigms are naturally complementary: GFlowNet can supply edge-level signals for diffusion to embed, while diffusion can guide broader exploration. Leveraging this synergy, we propose Diffusion-Enhanced GFlowNet (DEG), a novel framework that integrates GFlowNet with a diffusion model to encourage richer flow expansion toward high-reward regions and derive higher-quality solutions. Specifically, DEG exploits GFlowNet's inherent diversity to generate edge-specific backward signals, applies the stochastic noise schedule of diffusion to perturb these signals, and then denoises them within the GFlowNet paradigm. To further improve scalability, we introduce a specialized decoder capable of dynamically adapting to diverse problem scales. Extensive experimental evaluations on synthetic and real-world datasets, including instances with up to 10,000 nodes, demonstrate that DEG consistently achieves favorable performance compared to baseline methods.