PhD: Public Transport & Robust Optimization

I completed my PhD at the Institute for Numerical and Applied Mathematics at Georg-August-Universität Göttingen (2016–2019), combining two research areas: public transport optimization and robust optimization.

Public transport optimization investigates how to design better bus and train networks: finding timetables, routes, and schedules that maximize passenger convenience while keeping costs reasonable. The challenge is that these planning stages are interconnected: the optimal timetable depends on the line network, which depends on passenger demand, which depends on the timetable. My work focused on integrating these stages rather than solving them sequentially.

Robust optimization asks: what if we don't know exactly what will happen? In public transport, delays are inevitable. Instead of optimizing for perfect conditions, robust methods find solutions that perform well even when things go wrong. A timetable that stays functional when delays occur.

I designed, implemented, and tested optimization algorithms using mixed-integer programming (C++, Python, Gurobi), and also assisted lectures in Numerical Mathematics with over 100 participants.