<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>6764</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>723</pageFirst>
    <pageLast>728</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Timetable Sparsification by Rolling Stock Rotation Optimization</title>
    <abstract language="eng">Rolling stock optimization is a task that naturally arises by operating a railway system. It could be seen with different level of details. From a strategic perspective to have a rough plan which types of fleets to be bought to a more operational perspective to decide which coaches have to be maintained first. This paper presents a new approach to deal with rolling stock optimisation in case of a (long term) strike. Instead of constructing a completely new timetable for the strike period, we propose a mixed integer programming model that is able to choose appropriate trips from a given timetable to construct efficient tours of railway vehicles covering an optimized subset of trips, in terms of deadhead kilometers and importance of the trips. The decision which trip is preferred over the other is made by a simple evaluation method that is deduced from the network and trip defining data.</abstract>
    <parentTitle language="eng">Operations Research 2017</parentTitle>
    <identifier type="doi">10.1007/978-3-319-89920-6_96</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-65948</enrichment>
    <enrichment key="AcceptedDate">2017-11-01</enrichment>
    <author>Ralf Borndörfer</author>
    <submitter>Stanley Schade</submitter>
    <author>Matthias Breuer</author>
    <author>Boris Grimm</author>
    <author>Markus Reuther</author>
    <author>Stanley Schade</author>
    <author>Thomas Schlechte</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="traffic">Mathematics of Transportation and Logistics</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="grimm">Grimm, Boris</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="schade">Schade, Stanley</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="reuther">Reuther, Markus</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6594</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-11-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Timetable Sparsification by Rolling Stock Rotation Optimization</title>
    <abstract language="eng">Rolling stock optimization is a task that naturally arises by operating a railway system.&#13;
It could be seen with different level of details. From a strategic perspective to have a rough plan which types of fleets to be bought to a more operational perspective to decide which coaches have to be maintained first. This paper presents a new approach to deal with rolling stock optimisation in case of a (long term) strike. &#13;
Instead of constructing a completely new timetable for the strike period, we propose a mixed integer programming model that is able to choose appropriate trips from a given timetable to construct efficient tours of railway vehicles covering an optimized subset of trips, in terms of deadhead kilometers and importance of the trips. The decision which trip is preferred over the other is made by a simple evaluation method that is deduced from the network and trip defining data.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-65948</identifier>
    <author>Ralf Borndörfer</author>
    <submitter>Boris Grimm</submitter>
    <author>Matthias Breuer</author>
    <author>Boris Grimm</author>
    <author>Markus Reuther</author>
    <author>Stanley Schade</author>
    <author>Thomas Schlechte</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-63</number>
    </series>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="traffic">Mathematics of Transportation and Logistics</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="grimm">Grimm, Boris</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="schade">Schade, Stanley</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="reuther">Reuther, Markus</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6594/ZR-17-63.pdf</file>
  </doc>
  <doc>
    <id>6339</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-11-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pattern Detection For Large-Scale Railway Timetables</title>
    <abstract language="eng">We consider railway timetables of our industrial partner DB Fernverkehr AG that operates the ICE high speed trains in the long-distance passenger railway network of Germany. Such a timetable covers a whole year with 364 days and, typically, includes more than 45,000 trips. A rolling stock rotation plan is not created for the whole timetable at once. Instead the timetable is divided into regular invariant sections and irregular deviations (e.g. for public holidays). A separate rotation plan with a weekly period can then be provided for each of the different sections of the timetable. We present an algorithmic approach to automatically recognize these sections. Together with the supplementing visualisation of the timetable this method has shown to be very relevant for our industrial partner.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-63390</identifier>
    <enrichment key="SourceTitle">Appeared in: Proceedings of the IAROR conference RailLille 2017</enrichment>
    <author>Stanley Schade</author>
    <submitter>Stanley Schade</submitter>
    <author>Ralf Borndörfer</author>
    <author>Matthias Breuer</author>
    <author>Boris Grimm</author>
    <author>Markus Reuther</author>
    <author>Thomas Schlechte</author>
    <author>Patrick Siebeneicher</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-17</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>railway timetables</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>pattern detection</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="grimm">Grimm, Boris</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="schade">Schade, Stanley</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="reuther">Reuther, Markus</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6339/ZR-17-17.pdf</file>
  </doc>
  <doc>
    <id>6432</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pattern Detection For Large-Scale Railway Timetables</title>
    <abstract language="eng">We consider railway timetables of our industrial partner DB Fernverkehr AG that operates the ICE high speed trains in the long-distance passenger railway network of Germany. Such a timetable covers a whole year with 364 days and, typically, includes more than 45,000 trips. A rolling stock rotation plan is not created for the whole timetable at once. Instead the timetable is divided into regular invariant sections and irregular deviations (e.g. for public holidays). A separate rotation plan with a weekly period can then be provided for each of the different sections of the timetable. We present an algorithmic approach to automatically recognize these sections. Together with the supplementing visualisation of the timetable this method has shown to be very relevant for our industrial partner.</abstract>
    <parentTitle language="eng">Proceedings of the IAROR conference RailLille</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-63390</enrichment>
    <author>Stanley Schade</author>
    <submitter>Stanley Schade</submitter>
    <author>Ralf Borndörfer</author>
    <author>Matthias Breuer</author>
    <author>Boris Grimm</author>
    <author>Markus Reuther</author>
    <author>Thomas Schlechte</author>
    <author>Patrick Siebeneicher</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="grimm">Grimm, Boris</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="schade">Schade, Stanley</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="reuther">Reuther, Markus</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
