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Railway Rolling Stock Planning: Robustness Against Large Disruptions

Author

Listed:
  • Valentina Cacchiani

    (Department of Electronics, Computer Science, and Systems, University of Bologna, 40136 Bologna, Italy)

  • Alberto Caprara

    (Department of Electronics, Computer Science, and Systems, University of Bologna, 40136 Bologna, Italy)

  • Laura Galli

    (Department of Electronics, Computer Science, and Systems, University of Bologna, 40136 Bologna, Italy)

  • Leo Kroon

    (Rotterdam School of Management, Erasmus University Rotterdam, NL-3000 DR Rotterdam, The Netherlands; and Netherlands Railways, NL-3500 HA Utrecht, The Netherlands)

  • Gábor Maróti

    (Rotterdam School of Management, Erasmus University Rotterdam, NL-3000 DR Rotterdam, The Netherlands)

  • Paolo Toth

    (Department of Electronics, Computer Science, and Systems, University of Bologna, 40136 Bologna, Italy)

Abstract

In this paper we describe a two-stage optimization model for determining robust rolling stock circulations for passenger trains. Here robustness means that the rolling stock circulations can better deal with large disruptions of the railway system. The two-stage optimization model is formulated as a large mixed-integer linear programming (MILP) model. We first use Benders decomposition to determine optimal solutions for the LP-relaxation of this model. Then we use the cuts that were generated by the Benders decomposition for computing heuristic robust solutions for the two-stage optimization model. We call our method Benders heuristic . We evaluate our approach on the real-life rolling stock-planning problem of Netherlands Railways, the main operator of passenger trains in the Netherlands. The computational results show that, thanks to Benders decomposition, the LP-relaxation of the two-stage optimization problem can be solved in a short time for a representative number of disruption scenarios. In addition, they demonstrate that the robust rolling stock circulation computed heuristically has total costs that are close to the LP lower bounds. Finally, we discuss the practical effectiveness of the robust rolling stock circulation: When a large number of disruption scenarios were applied to these robust circulations and to the nonrobust optimal circulations, the former appeared to be much more easily recoverable than the latter.

Suggested Citation

  • Valentina Cacchiani & Alberto Caprara & Laura Galli & Leo Kroon & Gábor Maróti & Paolo Toth, 2012. "Railway Rolling Stock Planning: Robustness Against Large Disruptions," Transportation Science, INFORMS, vol. 46(2), pages 217-232, May.
    • Handle: RePEc:inm:ortrsc:v:46:y:2012:i:2:p:217-232
    DOI: 10.1287/trsc.1110.0388
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    References listed on IDEAS

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    1. Valentina Cacchiani & Alberto Caprara & Matteo Fischetti, 2012. "A Lagrangian Heuristic for Robustness, with an Application to Train Timetabling," Transportation Science, INFORMS, vol. 46(1), pages 124-133, February.
    2. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    3. Kroon, Leo & Maróti, Gábor & Helmrich, Mathijn Retel & Vromans, Michiel & Dekker, Rommert, 2008. "Stochastic improvement of cyclic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 553-570, July.
    4. Alan L. Erera & Juan C. Morales & Martin Savelsbergh, 2009. "Robust Optimization for Empty Repositioning Problems," Operations Research, INFORMS, vol. 57(2), pages 468-483, April.
    5. Fioole, Pieter-Jan & Kroon, Leo & Maroti, Gabor & Schrijver, Alexander, 2006. "A rolling stock circulation model for combining and splitting of passenger trains," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1281-1297, October.
    6. Matteo Fischetti & Domenico Salvagnin & Arrigo Zanette, 2009. "Fast Approaches to Improve the Robustness of a Railway Timetable," Transportation Science, INFORMS, vol. 43(3), pages 321-335, August.
    7. Joyce W. Yen & John R. Birge, 2006. "A Stochastic Programming Approach to the Airline Crew Scheduling Problem," Transportation Science, INFORMS, vol. 40(1), pages 3-14, February.
    8. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
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