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Coordinating maintenance windows and train traffic: a case study

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  • Tomas Lidén

    (Linköping University, Department of Science and Technology
    Swedish National Road and Transport Resarch Institute, Traffic Analysis and Logistics)

Abstract

This paper concerns a case study for optimal planning and coordination of railway maintenance windows and train traffic. The purpose is to validate a previously presented optimization model on a demanding real-life problem instance and to obtain results that apply in similar planning situations. A mixed integer linear programming model is used for a 913 km long, single-track railway line through the northern part of Sweden, with traffic consisting of 82 trains per day, most of which are freight trains. Cyclic 1-day schedules are produced, which show that 2 h long maintenance windows can be scheduled with small adjustments of the train traffic. The sensitivity for cost changes is studied, which shows that the train costs must increase by more than 30% in order to change the structure of the window solutions. Resource efficient window schedules are obtained by assigning maintenance teams to all windows while respecting crew work and rest time restrictions. A comparison with manually constructed plans from the Swedish Transport Administration indicates that larger window volumes can be scheduled at a lower cost and with solution structures which are deemed reasonable and useful as guidance for constructing the real window patterns. Finally, we estimate that using an integrated planning approach (where maintenance and trains are jointly planned) instead of a sequential approach (where a train timetable has precedence over the maintenance windows), will give maintenance cost savings of 11–17%, without incurring any large cost increases for the train traffic. The paper also presents a method for achieving cyclic schedules without any period-deciding variables, and discusses the consequences of the aggregated capacity usage model that has been adopted.

Suggested Citation

  • Tomas Lidén, 2020. "Coordinating maintenance windows and train traffic: a case study," Public Transport, Springer, vol. 12(2), pages 261-298, June.
  • Handle: RePEc:spr:pubtra:v:12:y:2020:i:2:d:10.1007_s12469-020-00232-2
    DOI: 10.1007/s12469-020-00232-2
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    References listed on IDEAS

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    1. J. I. van Zante--de Fokkert & D. den Hertog & F. J. van den Berg & J. H. M. Verhoeven, 2007. "The Netherlands Schedules Track Maintenance to Improve Track Workers’ Safety," Interfaces, INFORMS, vol. 37(2), pages 133-142, April.
    2. Peter Brucker & Silvia Heitmann & Sigrid Knust, 2005. "Scheduling railway traffic at a construction site," Springer Books, in: Hans-Otto Günther & Kap Hwan Kim (ed.), Container Terminals and Automated Transport Systems, pages 345-356, Springer.
    3. G Budai & D Huisman & R Dekker, 2006. "Scheduling preventive railway maintenance activities," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1035-1044, September.
    4. 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.
    5. Cacchiani, Valentina & Toth, Paolo, 2012. "Nominal and robust train timetabling problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 727-737.
    6. Natashia Boland & Mike Hewitt & Luke Marshall & Martin Savelsbergh, 2019. "The price of discretizing time: a study in service network design," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(2), pages 195-216, June.
    7. Maurice QUEYRANNE & Laurence A. WOLSEY, 2017. "Tight MIP formulations for bounded up/down times and interval-dependent start-ups," LIDAM Reprints CORE 2876, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Peng, Fan & Ouyang, Yanfeng, 2012. "Track maintenance production team scheduling in railroad networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1474-1488.
    9. Lucas P. Veelenturf & Martin P. Kidd & Valentina Cacchiani & Leo G. Kroon & Paolo Toth, 2016. "A Railway Timetable Rescheduling Approach for Handling Large-Scale Disruptions," Transportation Science, INFORMS, vol. 50(3), pages 841-862, August.
    10. Wen, M. & Li, R. & Salling, K.B., 2016. "Optimization of preventive condition-based tamping for railway tracks," European Journal of Operational Research, Elsevier, vol. 252(2), pages 455-465.
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