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Robustness and Recovery in Train Scheduling - a Case Study from DSB S-tog a/s

Authors: Mads Hofman, Line Madsen, Julie Jespersen Groth, Jens Clausen, and Jesper Larsen

Published in: OASIcs, Volume 5, 6th Workshop on Algorithmic Methods and Models for Optimization of Railways (ATMOS'06) (2006)


Abstract
This paper presents a simulation model to study the robustness of timetables of DSB S-tog a/s, the city rail of Copenhagen. Dealing with rush hour scenarios only, the simulation model investigates the effects of disturbances on the S-tog network. Several timetables are analyzed with respect to robustness. Some of these are used in operation and some are generated for the purpose of investigating timetables with specific alternative characteristics.

Cite as

Mads Hofman, Line Madsen, Julie Jespersen Groth, Jens Clausen, and Jesper Larsen. Robustness and Recovery in Train Scheduling - a Case Study from DSB S-tog a/s. In 6th Workshop on Algorithmic Methods and Models for Optimization of Railways (ATMOS'06). Open Access Series in Informatics (OASIcs), Volume 5, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


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@InProceedings{hofman_et_al:OASIcs.ATMOS.2006.687,
  author =	{Hofman, Mads and Madsen, Line and Jespersen Groth, Julie and Clausen, Jens and Larsen, Jesper},
  title =	{{Robustness and Recovery in Train Scheduling - a Case Study from DSB S-tog a/s}},
  booktitle =	{6th Workshop on Algorithmic Methods and Models for Optimization of Railways (ATMOS'06)},
  pages =	{1--22},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-01-9},
  ISSN =	{2190-6807},
  year =	{2006},
  volume =	{5},
  editor =	{Jacob, Riko and M\"{u}ller-Hannemann, Matthias},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2006.687},
  URN =		{urn:nbn:de:0030-drops-6878},
  doi =		{10.4230/OASIcs.ATMOS.2006.687},
  annote =	{Keywords: Train scheduling, simulation model, robustness, recovery}
}
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