Algorithmic decision support for train scheduling in a large and highly utilised railway network

Abstract

This thesis addresses the general problem of constructing train schedules, in particular for large and highly utilised railway networks. Commercial requirements for the timetable are assumed to be given, and the task is to provide detailed conflict-free track paths for each train that fulfil these requirements. In the thesis, a comprehensive approach from the commercial description of intended train services to a conflict-free detailed schedule for a whole day is developed. The methodology follows a divide-and-conquer strategy based on three description levels: the service intention, the macroscopic timetable, and the microscopic schedule. The levels are interfaced in such a way that planners have the possibility of intervening into the specifications on every level, and enabling a feedback loops for testing different alternative scenarios. Many models and algorithms for train scheduling have already been proposed in the literature, some of them with successful application in practice. However, they are either designed for large-scale problems, considering a simplified topology and safety system, or are detailed approaches, yet applicable only to a restricted area. This thesis combines both approaches for finding detailed schedules for large networks, partially relying known models and algorithms from the literature, adapted or extended, and partially developing totally new ideas and methods.