Neural Approaches for Multi-Objective Routing on Multigraphs

Preprint, 2025

Learning-based methods for routing have gained significant attention in recent years, both in single-objective and multi-objective contexts. Yet, existing methods are unsuitable for routing on multigraphs, which feature multiple edges with distinct attributes between node pairs, despite their strong relevance in real-world scenarios. In this paper, we propose two graph neural network-based methods to address multi-objective routing on multigraphs. Our first approach operates directly on the multigraph by autoregressively selecting edges until a tour is completed. The second model first simplifies the multigraph via a learned pruning strategy and then performs routing on the resulting simple graph. We evaluate both models empirically and demonstrate their strong performance across a range of problems and distributions.