Enhancing Cross-Problem Vehicle Routing via Federated Learning
Summary: arXiv:2604.10652v1 Announce Type: new
Abstract: Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has demonstrated superior efficiency over some traditional algorithms. While serving as a primary NCO approach for solving general VRPs, current cross-problem learning paradigms are still subject to performance degradation and generalizability decay, when transferring from simple VRP variants to those involving different and complex constraints. To strengthen the paradigms, this paper offers an innovative “Multi-problem Pre-train, then Single-problem Fine-tune” framework with Federated Learning (MPSF-FL). This framework exploits the common knowledge of a federated global model to foster efficient cross-problem knowledge sharing and transfer among local models for single-problem fine-tuning. In this way, local models effectively retain common VRP knowledge from up-to-date global model, while being efficiently adapted to downstream VRPs with heterogeneous complex constraints. Experimental results demonstrate that our framework not only enhances the performance in diverse VRPs, but also improves the generalizability in unseen problems.
Introduction
The logistics and supply chain industries face significant challenges in optimizing vehicle routing. Traditional vehicle routing problems have drawn considerable research attention, yet they often struggle with efficiency when dealing with complex constraints. Recent advancements in neural combinatorial optimization (NCO) have shown promise, but the need for improved generalizability across different problem variants remains a critical concern.
Challenges in Current Approaches
Current cross-problem learning paradigms experience several challenges:
- Performance Degradation: The effectiveness of existing models often diminishes when applied to more complex VRP variants.
- Generalizability Decay: Models trained on simpler problems struggle to adapt to the intricacies of varied constraints found in more complex scenarios.
- Knowledge Transfer Issues: Difficulty in sharing knowledge effectively across different problem domains hampers overall performance.
The MPSF-FL Framework
The proposed “Multi-problem Pre-train, then Single-problem Fine-tune” framework (MPSF-FL) addresses these challenges by utilizing federated learning principles:
- Multi-problem Pre-training: A federated global model is pre-trained on diverse VRPs, capturing essential patterns and common knowledge.
- Single-problem Fine-tuning: Local models adapt this global knowledge to the specifics of individual VRP tasks, allowing for fine-tuning that considers unique constraints.
- Efficient Knowledge Sharing: The federated model facilitates knowledge transfer, enabling local models to retain and leverage global insights while adapting to local conditions.
Experimental Results
Results from numerous experiments highlight the effectiveness of the MPSF-FL framework:
- Enhanced Performance: The framework consistently outperformed traditional methods across a variety of VRPs.
- Improved Generalizability: Models demonstrated better adaptability to unseen problems, showcasing the efficacy of knowledge sharing.
- Scalability: The framework is scalable, making it suitable for real-world applications involving diverse and complex routing challenges.
Conclusion
The introduction of the MPSF-FL framework represents a significant advancement in the field of vehicle routing problems. By leveraging federated learning, this approach not only enhances performance across different VRPs but also addresses the pressing issue of generalizability. As logistics and supply chain demands continue to evolve, such innovative frameworks will be crucial in optimizing operations and improving efficiency.