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  <doc>
    <id>7429</id>
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    <language>eng</language>
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    <publishedDate>2019-08-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Three-Phase Heuristic for Cyclic Crew Rostering with Fairness Requirements</title>
    <abstract language="eng">In this paper, we consider the Cyclic Crew Rostering Problem with Fairness Requirements (CCRP-FR). In this problem, attractive cyclic rosters have to be constructed for groups of employees, considering multiple, a priori determined, fairness levels. The attractiveness follows from the structure of the rosters (e.g., sufficient rest times and variation in work), whereas fairness is based on the work allocation  among the different roster groups. We propose a three-phase heuristic for the CCRP-FR, which  combines the strength of column generation techniques with a large-scale neighborhood search algorithm.  The design of the heuristic assures that good solutions for all fairness levels are obtained quickly, and can still be further improved if additional running time is available. We evaluate the performance of the algorithm using real-world data from Netherlands Railways, and show that the heuristic finds close to optimal solutions for many of the considered instances. In particular, we show that the heuristic is able to quickly find major improvements upon the current sequential practice: For most instances, the heuristic is  able to increase the attractiveness  by at least 20% in just a few minutes.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-74297</identifier>
    <author>Thomas Breugem</author>
    <submitter>Thomas Schlechte</submitter>
    <author>Ralf Borndörfer</author>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-43</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crew Planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Column Generation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Variable-Depth Neighborhood Search</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="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7429/ZR-19-43.pdf</file>
  </doc>
  <doc>
    <id>6695</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2018-02-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Conflict-free railway track assignment at depots</title>
    <abstract language="eng">Managing rolling stock with no passengers aboard is a critical component of railway operations. One aspect of managing rolling stock is to park the rolling stock on a given set of tracks at the end of a day or service. Depending on the parking assignment, shunting may be required in order for a parked train to depart or for an incoming train to park. Given a collection of tracks M and a collection of trains T with a fixed arrival-departure timetable, the train assignment problem (TAP) is to determine the maximum number of trains from T that can be parked on M according to the timetable and without the use of shunting. Hence, efficiently solving the TAP allows to quickly compute feasible parking schedules that do not require further shunting adjustments. In this paper, we show that the TAP is NP-hard and present two integer programming models for solving the TAP. We compare both models on a theoretical level. Moreover, to our knowledge, we consider the first approach that integrates track lengths along with the three most common types of parking tracks FIFO, LIFO and FREE tracks in a common model. Furthermore, to optimize against uncertainty in the arrival times of the trains we extend our models by stochastic and robust modeling techniques. We conclude by giving computational results for both models, observing that they perform well on real timetables.</abstract>
    <parentTitle language="eng">Journal of Rail Transport Planning &amp; Management</parentTitle>
    <identifier type="doi">10.1016/j.jrtpm.2017.12.004</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">epub ahead of print</enrichment>
    <enrichment key="FulltextUrl">https://www.sciencedirect.com/science/article/pii/S2210970617300574</enrichment>
    <author>Brady Gilg</author>
    <submitter>Thomas Schlechte</submitter>
    <author>Torsten Klug</author>
    <author>Rosemarie Martienssen</author>
    <author>Joseph Paat</author>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <author>Senan Seymen</author>
    <author>Alexander Tesch</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="klug">Klug, Torsten</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="tesch">Tesch, Alexander</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6385</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Conflict-Free Railway Track Assignment at Depots</title>
    <abstract language="eng">Managing rolling stock with no passengers aboard is a critical component of railway operations. In particular, one problem is to park the rolling stock on a given set of tracks at the end of a day or service. Depending on the parking assignment, shunting may be required in order for a parked train to depart or for an incoming train to park. Given a collection of tracks M and a collection of trains T with fixed arrival-departure timetable, the train assignment problem (TAP) is to determine the maximum number of trains from T that can be parked on M according to the timetable and without the use of shunting. Hence, efficiently solving the TAP allows to quickly compute feasible parking schedules that do not require further shunting adjustments. In this paper, we present two integer programming models for solving the TAP. To our knowledge, this is the first integrated approach that considers track lengths along with the three most common types of parking tracks. We compare these models on a theoretical level. We also prove that a decision version of the TAP is NP-complete, justifying the use of integer programming techniques. Using stochastic and robust modelling techniques, both models produce parking assignments that are optimized and robust according to random train delays. &#13;
We conclude with computational results for both models, observing that they perform well on real timetables.</abstract>
    <parentTitle language="eng">Proceedings of the IAROR conference RailLille</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-63843</enrichment>
    <author>Brady Gilg</author>
    <submitter>Alexander Tesch</submitter>
    <author>Torsten Klug</author>
    <author>Rosemarie Martienssen</author>
    <author>Joseph Paat</author>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <author>Sinan Seymen</author>
    <author>Alexander Tesch</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="klug">Klug, Torsten</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="tesch">Tesch, Alexander</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>5607</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <completedDate>2015-07-09</completedDate>
    <publishedDate>2015-07-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Duty Rostering in Public Transport - Facing Preferences, Fairness, and Fatigue</title>
    <abstract language="eng">Duty rostering problems occur in different application contexts and come in different flavors. They give rise to very large scale integer programs which ypically have lots of solutions and extremely fractional LP relaxations. In such a situation, heuristics&#13;
can be a viable algorithmic choice. We propose an mprovement method of the Lin-Kernighan type for the solution of duty rostering  problems.  We illustrate its versatility and solution quality on three different applications in public transit, vehicle routing, and&#13;
  airline rostering with a focus on the management of preferences, fairness, and fatigue, respectively.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-56070</identifier>
    <author>Ralf Borndörfer</author>
    <submitter>Thomas Schlechte</submitter>
    <author>Markus Reuther</author>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <author>Elmar Swarat</author>
    <author>Steffen Weider</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-44</number>
    </series>
    <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="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="persons" number="swarat">Swarat, Elmar</collection>
    <collection role="persons" number="weider">Weider, Steffen</collection>
    <collection role="projects" number="LHS-CS">LHS-CS</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="projects" number="TOLLCONTROLOPT">TOLLCONTROLOPT</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/5607/ZR-15-44.pdf</file>
  </doc>
  <doc>
    <id>6384</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>2017-05-03</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Conflict-Free Railway Track Assignment at Depots</title>
    <abstract language="eng">Managing rolling stock with no passengers aboard is a critical component of railway operations. In particular, one problem is to park the rolling stock on a given set of tracks at the end of a day or service. Depending on the parking assignment, shunting may be required in order for a parked train to depart or for an incoming train to park. Given a collection of tracks M and a collection of trains T with fixed arrival-departure timetable, the train assignment problem (TAP) is to determine the maximum number of trains from T that can be parked on M according to the timetable and without the use of shunting. Hence, efficiently solving the TAP allows to quickly compute feasible parking schedules that do not require further shunting adjustments. In this paper, we present two integer programming models for solving the TAP. To our knowledge, this is the first integrated approach that considers track lengths along with the three most common types of parking tracks. We compare these models on a theoretical level. We also prove that a decision version of the TAP is NP-complete, justifying the use of integer programming techniques. Using stochastic and robust modelling techniques, both models produce parking assignments that are optimized and robust according to random train delays. We conclude with computational results for both models, observing that they perform well on real timetables.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-63843</identifier>
    <author>Brady Gilg</author>
    <submitter>Alexander Tesch</submitter>
    <author>Torsten Klug</author>
    <author>Rosemarie Martienssen</author>
    <author>Joseph Paat</author>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <author>Sinan Seymen</author>
    <author>Alexander Tesch</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-23</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Depot Planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Railway Track Assignment</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="klug">Klug, Torsten</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="persons" number="tesch">Tesch, Alexander</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6384/ZR-17-23.pdf</file>
  </doc>
  <doc>
    <id>5729</id>
    <completedYear>2015</completedYear>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2015-07-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Duty Rostering in Public Transport - Facing Preferences, Fairness, and Fatigue</title>
    <abstract language="eng">Duty rostering problems occur in different application contexts and come in different flavors. They give rise to very large scale integer programs which ypically have lots of solutions and extremely fractional LP relaxations. In such a situation, heuristics can be a viable algorithmic choice. We propose an mprovement method of the Lin-Kernighan type for the solution of duty rostering problems. We illustrate its versatility and solution quality on three different applications in public transit, vehicle routing, and airline rostering with a focus on the management of preferences, fairness, and fatigue, respectively.</abstract>
    <parentTitle language="eng">Proceedings of Conference on Advanced Systems in Public Transport 2015 (CASPT2015)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-56070</enrichment>
    <author>Ralf Borndörfer</author>
    <submitter>Thomas Schlechte</submitter>
    <author>Markus Reuther</author>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <author>Elmar Swarat</author>
    <author>Steffen Weider</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="persons" number="swarat">Swarat, Elmar</collection>
    <collection role="persons" number="weider">Weider, Steffen</collection>
    <collection role="projects" number="LHS-CS">LHS-CS</collection>
    <collection role="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="projects" number="TOLLCONTROLOPT">TOLLCONTROLOPT</collection>
    <collection role="projects" number="VS-Rail">VS-Rail</collection>
    <collection role="projects" number="WS-OPT">WS-OPT</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>9366</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>other</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Bouquet of Features in Rolling Stock Rotation Planning</title>
    <abstract language="eng">Rolling stock is one of the major assets for a railway transportation company. Hence, their utilization should be as efficiently and effectively as possible. Railway undertakings are facing rolling stock scheduling challenges in different forms - from rather idealized weekly strategic problems to very concrete operational ones. Thus, a vast of optimization models with different features and objectives exist. Thorlacius et al. (2015) provides a comprehensive and valuable collection on technical requirements, models, and methods considered in&#13;
the scientific literature. We contribute with an update including recent works. The main focus of the paper is to present a classification and elaboration of the major features which our solver R-OPT is able to handle. Moreover, the basic optimization model and algorithmic ingredients of R-OPT are discussed. Finally, we present computational results for a cargo application at SBB CARGO AG and other railway undertakings for passenger traffic in Europe to show the capabilities of R-OPT.</abstract>
    <parentTitle language="deu">Conference Proceedings RailBelgrade 2023</parentTitle>
    <identifier type="url">https://easychair.org/publications/preprint/Nms6</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">epub ahead of print</enrichment>
    <enrichment key="AcceptedDate">06.03.2023</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Thomas Schlechte</author>
    <submitter>Ralf Borndörfer</submitter>
    <author>Christian Blome</author>
    <author>Stefan Gerber</author>
    <author>Stefan Hauser</author>
    <author>Jens Kasten</author>
    <author>Gilbert Müller</author>
    <author>Christof Schulz</author>
    <author>Michel Thüring</author>
    <author>Steffen Weider</author>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="persons" number="weider">Weider, Steffen</collection>
    <collection role="institutes" number="neo">Network Optimization</collection>
    <collection role="projects" number="MODAL-MobilityLab">MODAL-MobilityLab</collection>
  </doc>
  <doc>
    <id>9505</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>106186</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>154</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-02-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A three-phase heuristic for the Fairness-Oriented Crew Rostering Problem</title>
    <abstract language="eng">The Fairness-Oriented Crew Rostering Problem (FCRP) considers the joint optimization of attractiveness and fairness in cyclic crew rostering. Like many problems in scheduling and logistics, the combinatorial complexity of cyclic rostering causes exact methods to fail for large-scale practical instances. In case of the FCRP, this is accentuated by the additionally imposed fairness requirements. Hence, heuristic methods are necessary. We present a three-phase heuristic for the FCRP combining column generation techniques with variable-depth neighborhood search. The heuristic exploits different mathematical formulations to find feasible solutions and to search for improvements. We apply our methodology to practical instances from Netherlands Railways (NS), the main passenger railway operator in the Netherlands Our results show the three-phase heuristic finds good solutions for most instances and outperforms a state-of-the-art commercial solver.</abstract>
    <parentTitle language="eng">Computers &amp; Operations Research</parentTitle>
    <identifier type="doi">10.1016/j.cor.2023.106186</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">epub ahead of print</enrichment>
    <enrichment key="AcceptedDate">2023-03-06</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Thomas Breugem</author>
    <submitter>Thomas Schlechte</submitter>
    <author>Thomas Schlechte</author>
    <author>Christof Schulz</author>
    <author>Ralf Borndörfer</author>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="schlechte">Schlechte, Thomas</collection>
    <collection role="persons" number="schulz">Schulz, Christof</collection>
    <collection role="projects" number="WS-OPT">WS-OPT</collection>
    <collection role="institutes" number="neo">Network Optimization</collection>
  </doc>
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