IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-319-72153-8_10.html
   My bibliography  Save this book chapter

Optimization of Rolling Stock Rotations

In: Handbook of Optimization in the Railway Industry

Author

Listed:
  • Markus Reuther

    (LBW Optimization GmbH & Zuse Institute Berlin)

  • Thomas Schlechte

    (LBW Optimization GmbH & Zuse Institute Berlin)

Abstract

This chapter shows a successful approach how to model and optimize rolling stock rotations that are required for the operation of a passenger timetable. The underlying mathematical optimization problem is described in detail and solved by Rotation Optimizer for Railways (ROTOR), i.e., a complex optimization algorithm based on linear programming and combinatorial methods. ROTOR is used by DB Fernverkehr AG (DBF) in order to optimize intercity express (ICE) rotations for the European high-speed network. We focus on main modeling and solving components, i.e. a hypergraph model and a coarse-to-fine column generation approach. Finally, the chapter concludes with a complex industrial re-optimization application showing the effectiveness of the approach for real world challenges.

Suggested Citation

  • Markus Reuther & Thomas Schlechte, 2018. "Optimization of Rolling Stock Rotations," International Series in Operations Research & Management Science, in: Ralf Borndörfer & Torsten Klug & Leonardo Lamorgese & Carlo Mannino & Markus Reuther & Thomas Schlec (ed.), Handbook of Optimization in the Railway Industry, chapter 0, pages 213-241, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-72153-8_10
    DOI: 10.1007/978-3-319-72153-8_10
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Scheffler, Martin & Neufeld, Janis S. & Hölscher, Michael, 2020. "An MIP-based heuristic solution approach for the locomotive assignment problem focussing on (dis-)connecting processes," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 64-80.
    2. Valentina Cacchiani & Alberto Caprara & Paolo Toth, 2019. "An Effective Peak Period Heuristic for Railway Rolling Stock Planning," Transportation Science, INFORMS, vol. 53(3), pages 746-762, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isochp:978-3-319-72153-8_10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.