100%, Zurich, fixed-term.
Despite the excellent quality of public transport systems in Switzerland, the railway system needs to increase its performance (quality, for instance, travel time) and capacity (amount of services run) and attract more travellers to match the ambitious targets from policy and environmental goals. One key aspect of traditional railway transport systems is their plan, which is based on predetermined routes, lines, and scheduled times, with little possibility of adjusting to unplanned and unexpected circumstances. The project focuses on optimal planning and rescheduling of railway operations.
To do so, large-scale optimisation, typically MILP (Mixed Integer Linear Programming) approaches are used, but they scale poorly and are somehow not directly applicable in industrial practice. To approach those gaps, it is crucial to learn the real-life constraints, and exploiting advanced computational methods (including pre-computation and/or machine learning) to make the solution process faster and consistently optimal. This position is to address this gap, together with the two largest industrial players in this field in Switzerland.