Short Bio

Lukas Frühwirth is a first-year PhD student in the iCAIML Doctoral College at TU Wien, jointly supervised by Nysret Musliu and Stefan Woltran. He holds a Master’s degree in Logic and Computation and a Bachelor’s degree in Management & Entrepreneurship. During his Master’s studies, Lukas specialized in algorithmics and AI, with a focus on combinatorial optimization. His Master’s thesis introduced a novel approach for tram driver scheduling—Time Frame Rostering—which he developed in collaboration with a major public transport provider and presented at PATAT 2024.

Before starting his PhD, Lukas gained hands-on experience developing optimization algorithms in real-world scheduling environments. His research interests lie in automated problem solving, hyperheuristics, and AI-driven optimization. He is particularly passionate about bridging technical problem-solving with practical applications in production and operations.

PhD Project - Advanced Solving Techniques for Production Planning and Scheduling

Supervised by Nysret Musliu and Stefan Woltran

Lukas’ PhD project focuses on developing general and intelligent problem-solving techniques for complex production scheduling problems. These problems arise across a variety of industrial domains, where manual planning becomes increasingly difficult due to automation and system complexity. While existing optimization and AI methods perform well in specific settings, there is a strong need for more general and automated approaches that adapt to new and evolving problem structures. The PhD project will be sponsored by MCP GmbH, a Viennese company that implements industry-specific tools and planning algorithms to optimize practical production scheduling problems. MCP GmbH will further provide access to data and instances of novel production scheduling problems from the industry, that we will investigate in this project.

Expected Results:

• We expect to address new challenging production scheduling problems.
• We expect to provide novel problem-solving methods that hybridize exact techniques with learning techniques.
• We expect to identify innovative and general features for automated algorithm selection in production scheduling.

Publications and Conferences

Conference Proceedings

• L. Frühwirth and N. Musliu, “Exact Methods for the Time Frame Rostering Problem in the Context of Tram Driver Scheduling,” in Proceedings of the 14th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2024), 2024, pp. 187-213, Online.

Presentations

• “Exact Methods for the Time Frame Rostering Problem in the Context of Tram Driver Scheduling”. PATAT 2024, Technical University of Denmark, Copenhagen. August 29, 2024