We propose new AI-based methods that include interactions between data-driven optimization and agent-based simulation to explore the trade-off between green, efficient, and reliable plans. This includes the operator-based perspective, i.e. the efficient usage of rolling stock and staff through optimized plans, as well as the railway network perspective, which is addressed through detailed agent-based simulation of plans to assess efficiency and reliability of planning results in a realistic railway network environment. The green aspect focuses on analyzing and predicting the energy consumption of the rolling stock under realistic conditions with AI-based methods extending existing approaches such as simheuristics.

Application period until 10 April 2022.