Rescheduling rolling stock during a disruption is a passenger railway optimization problem. In current practice this is typically optimized manually despite the high complexity and
high runtime requirements of the task. In this paper we propose a path-based mathematical
formulation that is solved using column generation in a complete Branch-and-Price framework. In contrast to
flow-based approaches our formulation is more easily extended to handle
certain families of constraints, such as train unit maintenance restrictions. We benchmark the
framework against real-life instances provided by the suburban railway operator in Copenhagen (DSB S-tog). In combination with a lower bound method we show that near-optimal
solutions can be found within a few seconds during a disruption. In addition we show that
framework is also able to find solution within a few minutes for non-disturbed timetables.