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Algorithmic Support for Railway Disruption Management

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  • Kroon, L.G.
  • Huisman, D.

Abstract

Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market.

Suggested Citation

  • Kroon, L.G. & Huisman, D., 2009. "Algorithmic Support for Railway Disruption Management," Econometric Institute Research Papers EI 2009-50, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:17524
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    References listed on IDEAS

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    1. Kohl, Niklas & Larsen, Allan & Larsen, Jesper & Ross, Alex & Tiourine, Sergey, 2007. "Airline disruption management—Perspectives, experiences and outlook," Journal of Air Transport Management, Elsevier, vol. 13(3), pages 149-162.
    2. Potthoff, D. & Huisman, D. & Desaulniers, G., 2008. "Column generation with dynamic duty selection for railway crew rescheduling," Econometric Institute Research Papers EI 2008-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    4. D'Ariano, Andrea & Pacciarelli, Dario & Pranzo, Marco, 2007. "A branch and bound algorithm for scheduling trains in a railway network," European Journal of Operational Research, Elsevier, vol. 183(2), pages 643-657, December.
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    7. 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.
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    Cited by:

    1. Daniel Potthoff & Dennis Huisman & Guy Desaulniers, 2010. "Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling," Transportation Science, INFORMS, vol. 44(4), pages 493-505, November.
    2. Hoogervorst, R. & Dollevoet, T.A.B. & Maróti, G. & Huisman, D., 2018. "Reducing Passenger Delays by Rolling Stock Rescheduling," Econometric Institute Research Papers EI2018-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Cadarso, Luis & Marín, Ángel & Maróti, Gábor, 2013. "Recovery of disruptions in rapid transit networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 53(C), pages 15-33.
    4. Jian Gang Jin & Kwong Meng Teo & Amedeo R. Odoni, 2016. "Optimizing Bus Bridging Services in Response to Disruptions of Urban Transit Rail Networks," Transportation Science, INFORMS, vol. 50(3), pages 790-804, August.

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