Lucas Kletzander

Lucas Kletzander is a PostDoc Researcher at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology. He teaches courses in theoretical computer science and project-based programming while leading research in scheduling optimization through the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (ARTIS project).

Education:

• Bachelor of Science (BSc)
• Diplom-Ingenieur (Dipl.-Ing.) in Engineering
• Doctor of Technical Sciences (Dr.techn.)

His research centers on solving complex personnel scheduling problems using artificial intelligence and operations research. Kletzander specializes in hyper-heuristics for cross-domain optimization, multi-objective workforce scheduling, and real-world applications like bus driver rostering and physician shift planning during pandemics. His work bridges theoretical algorithm development with industrial implementation, particularly in transportation and healthcare sectors.

Recent publications reveal a strong trajectory toward reinforcement learning for hyper-heuristics and multi-objective optimization in scheduling. He pioneers instance space analysis to characterize problem difficulty and develops hybrid methods combining column generation with large neighborhood search. His research consistently addresses practical constraints like complex break rules in transportation scheduling.

Advising and Grants:

• Supervised master's theses on explainability in rotating workforce scheduling and deep reinforcement learning for hyper-heuristics
• Principal investigator in the Christian Doppler Laboratory ARTIS (2017-2025) funded by the CDG
• Collaborates with industry partners on bus scheduling and aircraft maintenance routing systems

Kletzander actively contributes to the Databases and Artificial Intelligence research group, where he develops decision support systems for automated scheduling and investigates the integration of constraint programming with metaheuristics for real-world optimization challenges.