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Fast Approaches to Improve the Robustness of a Railway Timetable

Author

Listed:
  • Matteo Fischetti

    (Department of Information Engineering, University of Padova, 1 35131-Padova, Italy)

  • Domenico Salvagnin

    (Department of Pure and Applied Mathematics, University of Padova, 1 35121-Padova, Italy)

  • Arrigo Zanette

    (Department of Pure and Applied Mathematics, University of Padova, 1 35121-Padova, Italy)

Abstract

The train timetabling problem (TTP) consists of finding a train schedule on a railway network that satisfies some operational constraints and maximizes some profit function that accounts for the efficiency of the infrastructure usage. In practical cases, however, the maximization of the objective function is not enough, and one calls for a robust solution that is capable of absorbing, as much as possible, delays/disturbances on the network. In this paper we propose and computationally analyze four different methods to improve the robustness of a given TTP solution for the aperiodic (noncyclic) case. The approaches combine linear programming (LP) and ad hoc stochastic programming/robust optimization techniques. We computationally compare the effectiveness and practical applicability of the four techniques under investigation on real-world test cases from the Italian railway company Trenitalia. The outcome is that two of the proposed techniques are very fast and provide robust solutions of comparable quality with respect to the standard (but very time consuming) stochastic programming approach.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:43:y:2009:i:3:p:321-335
    DOI: 10.1287/trsc.1090.0264
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    References listed on IDEAS

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    1. Alberto Caprara & Matteo Fischetti & Paolo Toth, 2002. "Modeling and Solving the Train Timetabling Problem," Operations Research, INFORMS, vol. 50(5), pages 851-861, October.
    2. Charles E. Clark, 1961. "Importance Sampling in Monte Carlo Analyses," Operations Research, INFORMS, vol. 9(5), pages 603-620, October.
    3. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    4. Kroon, L.G. & Dekker, R. & Vromans, M.J.C.M., 2005. "Cyclic Railway Timetabling: a Stochastic Optimization Approach," ERIM Report Series Research in Management ERS-2005-051-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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