Algorithms for Robust and online Railway optimization: Improving the Validity and reliability of large scale systems
FP6 IST/FET STREP Project IST-021235-2
Project web site
Start-End Dates 01/02/06 - 31/01/2009
Coordinator Zaroliagis, Christos (CTI)
  • Research Academic Computer Tecnology Institute (CTI)
  • Faculty of Informatics, University of Karlsruhe (UniKarl)
  • Erasmus University Rotterdam (EUR)
  • Eidgenössische Technische Hochschule Zürich (ETHZ)
  • Dipartimento di Ingegneria Elettrica, University of L’Aquila (ULA)
  • Department of Mathematics and Computer Science, Technische Universiteit Eindhoven (TUE)
  • Combinatorial Optimization & Graph Algorithms group, Technical University Berlin (TUB)
  • Department of Electronics, Computer Science and Automation, University of Bologna (UniBo)
  • Department of Information Engineering, University of Padua (DEI)
  • Georg-August University of Goettingen (UniGoe)
  • University of Sevilla (USE)
  • Polytechnic University of Valencia (UPVLC)
  • Société Nationale de Chemins-de-Fer Français (SNCF)


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.

Our main goal is to develop the necessary foundational algorithmic research in order to provide ingenious and sound answers to the fundamental efficiency and quality issues encapsulated in robust and online planning of complex, large-scale systems as those in railways. We will endeavour to develop a thorough understanding of the fundamental issues that make robust and online railway optimization problems hard and to subsequently develop new algorithmic and complexity principles to deal with hardness through an integrated, interdisciplinary approach drawn from algorithmic, operations research, and constraint programming techniques.


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