15 citations · 20 across the 7 of their papers we have counts for
9 papers
GREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions
Attila Lischka, Ze Zhou, Xuegang +2
Online, same-day delivery requires operators to constantly update their routes, and the routing cost for deliveries increases with the number of customers. Out-of-home (OOH) delive…
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, Simon Rauch +5
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…
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.…
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.…
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…