ARRIVAL: Algorithms for Robust and online Railway optimization: Improving the Validity and reliAbility of Large scale systems

In this project, we are interested in establishing such a new paradigm and considerably advance the current state of algorithmic research by attacking optimization questions in perhaps the most complex and largest in scale (transportation) setting: that of railway systems.

Algorithmic methods have reached a state of maturity as a consequence of decades of research where real-world problems were posed to the algorithms community triggering important developments in the field. Despite this success, the current state of algorithmic research still faces severe difficulties, or cannot cope at all, with highly-complex and data-intensive applications as those dealing with optimization issues in large-scale communication and transportation networks. The complexity and size of such optimization problems pose new challenges for algorithmic research, and their efficient solution requires a radically new foundational paradigm.

In this project, we are interested in establishing such a new paradigm and considerably advance the current state of algorithmic research by attacking optimization questions in perhaps the most complex and largest in scale (transportation) setting: that of railway systems. Railway optimization deals with planning and scheduling problems over several time horizons. We focus on two important and actually unexplored facets of planning that pose even harder optimization questions: robust planning and online (real-time) planning. These two, tightly coupled, facets constitute a proactive and a reactive approach, respectively, to deal with disruptions to the normal operation.