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Solving large scale crew scheduling problems in practice

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Abstract

This paper deals with large-scale crew scheduling problems arising at the main Dutch railway operator, Netherlands Railways (NS). NS operates about 30000 trains a week. All these trains need a driver and a certain number of guards. Some labor rules restrict the duties of a certain crew base over the complete week. Therefore, splitting the problem in several subproblems per day leads to suboptimal solutions.

In this paper, we present an algorithm, called LUCIA, which can solve such huge instances without splitting. This algorithm combines Lagrangian heuristics, column generation and fixing techniques. We compare the results with existing practice. The results show that the new method significantly improves the solution.

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Notes

  1. Intel Octo Core (2×4 Cores), 3.0 GHz, 8 Gb RAM.

References

  • Abbink E, Fischetti M, Kroon L, Timmer G, Vromans M (2005) Reinventing crew scheduling at Netherlands Railways. Interfaces 35:393–401

    Article  Google Scholar 

  • Abbink E, Van ’t Wout J, Huisman D (2007) Solving large scale crew scheduling problems by using iterative partitioning. In: Liebchen C, Ahuja RK, Mesa JA (eds) ATMOS 2007—7th workshop on algorithmic approaches for transportation modeling, optimization, and systems. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany. http://drops.dagstuhl.de/opus/volltexte/2007/1168. ISBN 978-3-939897-04-0

    Google Scholar 

  • Barnhart C, Johnson EL, Nemhauser GL, Savelsbergh MWP, Vance PH (1998) Branch-and-price: column generation for solving huge integer programs. Oper. Res. 46:316–329

    Article  Google Scholar 

  • Caprara A, Fischetti M, Toth P (1999) A heuristic algorithm for the set covering problem. Oper Res 47:730–743

    Article  Google Scholar 

  • de Groot SW, Huisman D (2008) Vehicle and crew scheduling: solving large real-world instances with an integrated approach. In: Hickman M, Mirchandani P, VoßS (eds) Computer-aided systems in public transport. Lecture notes in economics and mathematical systems, vol 600. Springer, Berlin, pp 43–56

    Chapter  Google Scholar 

  • Desaulniers G, Desrosiers J, Solomon MM (eds) (2005) Column generation. Springer, New York

    Google Scholar 

  • Hartog A, Huisman D, Abbink EJW, Kroon LG (2009) Decision support for crew rostering at NS. Public Transport 1:121–133

    Article  Google Scholar 

  • Huisman D (2007) A column generation approach to solve the crew re-scheduling problem. Eur J Oper Res 180:163–173

    Article  Google Scholar 

  • Huisman D, Kroon LG, Lentink RM, Vromans MJCM (2005) Operations research in passenger railway transportation. Stat Neerl 59:467–497

    Article  Google Scholar 

  • Kohl N (2003) Solving the world’s largest crew scheduling problem. ORbit 8–12

  • Kroon LG, Fischetti M (2001) Crew scheduling for Netherlands railways “Destination: Customer”. In: VoßS, Daduna JR (eds) Computer-aided scheduling of public transport. Springer, Berlin, pp 181–201

    Chapter  Google Scholar 

  • Lübbecke ME, Desrosiers J (2005) Selected topics in column generation. Oper Res 53:1007–1023

    Article  Google Scholar 

  • Mesquita M, Paias A, Respício A (2009) Branching approaches for integrated vehicle and crew scheduling. Public Transport 1:21–37

    Article  Google Scholar 

  • Morgado EM, Martins JP (1998) CREWS_NS: scheduling train crews in The Netherlands. AI Mag 19:25–38

    Google Scholar 

  • Rezanova NJ, Ryan DM (2010) The train driver recovery problem—a set partitioning based model. Comput Oper Res 37:845–856

    Article  Google Scholar 

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Acknowledgements

We want to thank Tiago Dias and Rudi Araújo for their important contribution on the implementation of the software code related with preparation of data, in particular the generation of connections.

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Authors and Affiliations

  1. Process Quality & Innovation, Netherlands Railways (NS), P.O. Box 2025, 3500 HA, Utrecht, The Netherlands

    E. J. W. Abbink, T. Dollevoet & D. Huisman

  2. Erasmus Center for Optimization in Public Transport (ECOPT) & Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands

    T. Dollevoet & D. Huisman

  3. SISCOG—Sistemas Cognitivos, SA, Campo Grande 378-33°, 1700-097, Lisboa, Portugal

    L. Albino, J. Roussado & R. L. Saldanha

Authors
  1. E. J. W. Abbink
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  2. L. Albino
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  3. T. Dollevoet
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  4. D. Huisman
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  5. J. Roussado
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  6. R. L. Saldanha
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Corresponding author

Correspondence to E. J. W. Abbink.

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Abbink, E.J.W., Albino, L., Dollevoet, T. et al. Solving large scale crew scheduling problems in practice. Public Transp 3, 149–164 (2011). https://doi.org/10.1007/s12469-011-0045-x

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  • DOI: https://doi.org/10.1007/s12469-011-0045-x

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