Every day a significant number of people choose for the railways as a comfortable and sustainable way of transportation. In order to accommodate the journeys of a large number of railway passengers, extensive planning is necessary. Unfortunately, the execution of the plans is frequently disrupted by unexpected events. For railway operators it is quite a challenge to deal with these disruptions as even small deviations from the plan can have large influences on the timetable, the rolling stock schedule and the crew schedule. More severely, these events reduce the available transport capacity and interrupt the mobility of the passengers.
This thesis discusses several models and solution approaches for railway disruption management based on algorithmic techniques from Operations Research. The main focus is to reduce the inconvenience passengers experience during disruptions. This is achieved by improving the disruption management approaches for timetable, rolling stock and crew rescheduling proposed within the scientific community. The existing models are extended by introducing greater flexibility, e.g. allowing small delays in the crew rescheduling or addition stops in the rolling stock rescheduling. As a result fewer trains are cancelled during disruptions and passengers have more options to reach their destination. Although some inconvenience will remain, as much as possible is mitigated.