5 papers
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…
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…
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…
Directly Follows Graphs Go Predictive Process Monitoring With Graph Neural Networks
Attila Lischka, Simon Rauch, Oliver Stritzel
In the past years, predictive process monitoring (PPM) techniques based on artificial neural networks have evolved as a method to monitor the future behavior of business processes.…