Enhancing Cross-Problem Vehicle Routing via Federated Learning

TL;DR

This paper introduces a federated learning framework that improves the transferability and performance of neural combinatorial optimization models across diverse vehicle routing problems with complex constraints.

Contribution

It proposes the MPSF-FL framework that leverages federated global models for effective cross-problem knowledge sharing and adaptation in VRPs.

Findings

Enhances performance across diverse VRPs.

Improves generalizability to unseen problems.

Efficiently adapts to complex VRP constraints.

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.