Template-based re-optimization of rolling stock rotations
Boris Grimm (),
Markus Reuther () and
Additional contact information
Ralf Borndörfer: Zuse Institute Berlin
Boris Grimm: Zuse Institute Berlin
Markus Reuther: Zuse Institute Berlin
Thomas Schlechte: Zuse Institute Berlin
Public Transport, 2017, vol. 9, issue 1, 365-383
Abstract Rolling stock, i.e., the set of railway vehicles, is among the most expensive and limited assets of a railway company and must be used efficiently. We consider in this paper the re-optimization problem to recover from unforeseen disruptions. We propose a template concept that allows to recover cost minimal rolling stock rotations from reference rotations under a large variety of operational requirements. To this end, connection templates as well as rotation templates are introduced and their application within a rolling stock rotation planning model is discussed. We present an implementation within the rolling stock rotation optimization framework rotor and computational results for scenarios provided by DB Fernverkehr AG, one of the leading railway operators in Europe.
Keywords: Rolling stock rotation problem; Re-optimization; Hypergraph-based integer programming; Rotation patterns (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s12469-017-0152-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:pubtra:v:9:y:2017:i:1:d:10.1007_s12469-017-0152-4
Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12469
Access Statistics for this article
Public Transport is currently edited by Stefan Voß
More articles in Public Transport from Springer
Bibliographic data for series maintained by Sonal Shukla ().