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.

In this project, the aim was to establish 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.

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