At present micro-level simulation tools cannot solve all conflicts during a simulation run but they provide a framework to support the user in checking different solutions to fix operational problems. As there are many constraints to be considered during an optimization of operations, the most suitable solutions depend strongly on the settings considered by the user. Moreover, the parameters might be interpreted by different users in different ways. Each stakeholder is normally only interested in finding the best solution for their part of the operation. The definition of an overall optimum is rather difficult and has been, therefore, a topic of several research activities. Starting with an annual timetable for passenger services, this part of operation is rather stable. On the other hand, cargo traffic requires different time slots varying from day to day. Since market access for both passenger and cargo services has to be handled in a fair way to provide the maximum amount of time slots and they are also responsible for punctual operation. Therefore, optimization algorithms have to operate based on these requirements.
The Micro-level simulation tool OpenTrack allows the user to deal with dispatching rules on a text based logical format. The difficulty in application is often to translate an operational rule into a logical term. Furthermore, the application programming interface (API) offers one the opportunity to influence the simulation run at any second of its calculation. Figure on the left illustrates the two-way communication between OpenTrack and any 3rd party tool. Using this unique feature it is possible to reschedule trains by giving speed aspects to be respected. This is now the key feature for applying a suitable algorithm for optimization. Kronecker Algebra has been identified as a promising algorithm for optimization of railway operation even for online application. In the GoSAFE RAIL project this feature will be used as a kind of dispatcher, and we will simulate the behaviour of actual network performance to prove that our optimization algorithm provides suitable solutions.