7 papers
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…
GREAT-EER: Graph Edge Attention Network for Emergency Evacuation Responses
Attila Lischka, Balázs Kulcsár
Emergency situations that require the evacuation of urban areas can arise from man-made causes (e.g., terrorist attacks or industrial accidents) or natural disasters, the latter be…
Learning to Dial-a-Ride: A Deep Graph Reinforcement Learning Approach to the Electric Dial-a-Ride Problem
Sten Elling Tingstad Jacobsen, Attila Lischka, Balázs Kulcsár +1
Urban mobility systems are transitioning toward electric, on-demand services, creating operational challenges for fleet management under energy and service-quality constraints. The…
Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Oliver Stritzel, Nick Hühnerbein, Nick Hühnerbein +6
In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the future behavior of unfolding business pr…
A GREAT Architecture for Edge-Based Graph Problems Like TSP
Attila Lischka, Filip Rydin, Jiaming Wu +2
In the last years, an increasing number of learning-based approaches have been proposed to tackle combinatorial optimization problems such as routing problems. Many of these approa…
Learning for routing: A guided review of recent developments and future directions
Fangting Zhou, Attila Lischka, Balazs Kulcsar +3
This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the t…