Stefan Zahlner

Alberto Castagna, Stefan Zahlner, Adrian Egli et al.

Managing disruptions in railway traffic management is a major challenge. Rising traffic density and infrastructure limits increase complexity, making the Vehicle Routing and Scheduling Problem (VRSP) difficult to solve reliably and in real time. While Operational Research (OR) methods are widely used, most dispatching still relies on human expertise due to the problem's exponential combinatorial complexity. Reinforcement Learning (RL) has gained attention for its potential in multi-agent coordination, but existing RL approaches often underperform OR methods and struggle to scale in dense rail networks. This paper addresses this gap from a machine learning perspective by introducing a semi-hierarchical RL formulation tailored to operational railway constraints. The method separates dispatching from routing through dedicated action and observation spaces, enabling policies to specialise in distinct decision scopes and addressing the imbalance between rare dispatch decisions and frequent routing updates. The approach is evaluated on the Flatland-RL simulator across five difficulty levels and 50 random seeds, with 7 to 80 trains. Results show substantially improved coordination, resource utilisation, and robustness compared with heuristic baselines and monolithic RL, nearly doubling the number of trains reaching their destinations, while keeping deadlock rates below 5% and adaptively sequencing, delaying, or cancelling trains under heavy congestion.

Yassine El Manyari, Anton R. Fuxjager, Stefan Zahlner et al.

Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. Effective grid topology control requires advanced tools capable of handling multi-objective trade-offs. While Reinforcement Learning (RL) offers a promising framework for tackling such challenges, existing Multi-Objective Reinforcement Learning (MORL) approaches fail to scale to the large state and action spaces inherent in real-world grid operations. Here we present a two-phase, efficient and scalable Multi-Objective Optimisation (MOO) method designed for grid topology control, combining an efficient RL learning phase with a rapid planning phase to generate day-ahead plans for unseen scenarios. We validate our approach using historical data from TenneT, a European Transmission System Operator (TSO), demonstrating minimal deployment time, generating day-ahead plans within 4-7 minutes with strong performance. These results underline the potential of our scalable method to support real-world power grid management, offering a practical, computationally efficient, and time-effective tool for operational planning. Based on current congestion costs and inefficiencies in grid operations, adopting our approach by TSOs could potentially save millions of euros annually, providing a compelling economic incentive for its integration in the control room.