Learning real-life constraints and objectives to determine timetables optimised for robustness interactively

Timetables are susceptible to minor disruptions that reveal a gap between target and actual performance. Timetables are often created based on target performance. We aim to provide a decision-making tool to find the best timetable for actual performance, enabling the creation of robust timetables with limited additional workload.