activity
20242026
most citedLess Is More -- On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

math.OC2026

Joint Planning and Scheduling of Modular Vehicles for Passenger-Freight Integration

Wanru Chen, Jiaming Wu, Balázs Kulcsár

This paper proposes a modular vehicle system for passenger-freight integration along a bidirectional transit corridor. The system uses homogeneous units that can be coupled into ve…

math.OC2025

Collaborative Charging Scheduling via Balanced Bounding Box Methods

Fangting Zhou, Balazs Kulcsar, Jiaming Wu

Electric mobility faces several challenges, most notably the high cost of infrastructure development and the underutilization of charging stations. The concept of shared charging o…

cs.LG2025

Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs

Filip Rydin, Attila Lischka, Jiaming Wu +2

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 fo…

cs.LG2024★ 2 cited

GREAT: A Graph Edge Attention Network for the Electric Vehicle Routing Problem with Asymmetric Costs

Attila Lischka, Filip Rydin, Jiaming Wu +2

Due to the ongoing electrification of transportation systems, energy-based routing problems such as the Electric Vehicle Routing Problem (EVRP) are becoming increasingly important.…

cs.LG2024★ 3 cited

Less Is More -- On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP

Attila Lischka, Jiaming Wu, Rafael Basso +2

Most of the recent studies tackling routing problems like the Traveling Salesman Problem (TSP) with machine learning use a transformer or Graph Neural Network (GNN) based encoder a…