Simulation And Optimization Of Traction Unit Circulations

The planning of traction unit circulations in a railway network is a very time-consuming task. In order to support the planning personnel, the paper proposes a combination of optimization, simulation and machine learning. This ensemble creates mathematically nearly optimal circulations that are also feasible in real operating procedures. An agent-based simulation model is developed that tests the circulation for its robustness against delays. The delays introduced into the system are based on predictions from a machine learning model built upon historical operational data. The paper first presents the used data and the delay prediction. Afterwards, the modeling and simulation part and the optimization are presented. At last, the interaction of simulation and optimization are described and promising results of a test case are shown.