Learning to Solve Multi-Objective Routing Problems on Multigraphs
Preprint, 2025

Learning-based methods for routing have gained significant attention in recent years, both in single-objective and multi-objective contexts. However, the multigraph setting, where multiple paths with distinct attributes can exist between destinations, has largely been overlooked, despite its high practical relevancy. In this paper, we introduce two neural approaches to address multi-objective routing on multigraphs. Our first approach works directly on the multigraph, by autoregressively selecting edges until a tour is completed. On the other hand, our second model first prunes the multigraph into a simple graph and then builds routes. We validate both models experimentally and find that they demonstrate strong performance across a variety of problems, including the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP).

Graph Neural Networks

Traveling Salesman Problem

Routing Problems

Multigraphs

Multi-Objective Optimization

Machine Learning

Graph Learning

Författare

Filip Rydin

Chalmers, Elektroteknik, System- och reglerteknik

Attila Lischka

Chalmers, Elektroteknik, System- och reglerteknik

Jiaming Wu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Morteza Haghir Chehreghani

Data Science och AI 2

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

LEAR: Robust LEArning methods for electric vehicle Route selection

Swedish Electromobility Centre, 2023-01-01 -- 2026-12-31.

Styrkeområden

Transport

Ämneskategorier (SSIF 2025)

Transportteknik och logistik

Diskret matematik

Artificiell intelligens

Infrastruktur

Chalmers e-Commons (inkl. C3SE, 2020-)

DOI

10.48550/arXiv.2506.22095

Mer information

Senast uppdaterat

2025-07-02