The MODAL:RailLab cooperates with DB Fernverkehr to develop an optimization core that helps to operate the Intercity-Express (ICE), Germany's fastest and most prestigious train, in the most efficient way. This is achieved by determining how the ICEs should rotate within Germany and, thereby, reducing the number of empty trips. The software has now been deployed in production at DB Fernverkehr for two years.
Deutsche Bahn (DB) is Germany's major railway company. It transports on average 5.4 million customers every day over a rail network that consists of 33,500 km of track, and 5,645 train stations. DB operates in over 130 countries world-wide. It provides its customers with mobility and logistical services, and operates and controls the related rail, road, ocean and air traffic networks.
You will learn to think about the railway network at DB from a planner's perspective. Making up ICE rotations sounds easy at first, but you will soon find out that a lot of constraints have to be taken into account and do not forget about the size of Germany's rail network! This makes finding and understanding suitable mathematical programming models a difficulty of its own. It will be your daily business to deal with huge data sets. You will write scripts to process the data and extract useful information. At your option you can come up with your own ideas and propose and implement extensions for our optimization core. The past project assignments included to find out how robust optimization methodology can be incorporated in the optimization process and to develop a rotation plan for the situation that a restricted amount of train conductors is available, e.g. in a strike scenario.
The program lasts for 8 weeks and should result in a comprehensive report which has to be presented at the end. It requires a high commitment, even more so if knowledge gaps have to be closed along the way.
The prospective participant should:
- have a good command of a high-level programming language (preferably C++) and experience in writing scripts, e.g. in Python or Shell,
- have attended classes in the area of combinatorial optimization, linear and integer programming or acquired the foundations of this field by some other means
- be prepared to work with huge datasets from industry partners (which involves cleaning and preprocessing to overcome inconsistencies and incompleteness).
Ideally he or she
- is familiar with procedures in the area of rail traffic and/or logistics,
- has experience in working in a Linux/Unix environment and
- collaborative work on source code (e.g. working with revision control systems).