IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v85y2016icp70-88.html
   My bibliography  Save this article

Disruption risk management in railroad networks: An optimization-based methodology and a case studyAuthor-Name: Azad, Nader

Author

Listed:
  • Hassini, Elkafi
  • Verma, Manish

Abstract

We propose an optimization-based methodology for recovery from random disruptions in service legs and train services in a railroad network. A network optimization model is solved for each service leg to evaluate a number of what-if scenarios. The solutions of these optimization problems are then used in a predictive model to identify the critical disruption factors and accordingly design a suitable mitigation strategy. A mitigation strategy, such as adding flexible or redundant capacity in the network, is an action that is deliberately taken by management in order to hedge against the cost and impact of disruption if it occurs. It is important that managers consider the trade-offs between the cost of mitigation strategy and the expected cost of disruption. The proposed methodology is applied to a case study built using the realistic infrastructure of a railroad network in the mid-west United States. The resulting analysis underscores the importance of accepting a slight increase in pre-disruption transportation costs, which in turn will enhance network resiliency by building dis-similar paths for train services, and by installing alternative links around critical service legs.

Suggested Citation

  • Hassini, Elkafi & Verma, Manish, 2016. "Disruption risk management in railroad networks: An optimization-based methodology and a case studyAuthor-Name: Azad, Nader," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 70-88.
  • Handle: RePEc:eee:transb:v:85:y:2016:i:c:p:70-88
    DOI: 10.1016/j.trb.2016.01.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261516000059
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.trb.2016.01.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Louwerse, Ilse & Huisman, Dennis, 2014. "Adjusting a railway timetable in case of partial or complete blockades," European Journal of Operational Research, Elsevier, vol. 235(3), pages 583-593.
    2. Leo Kroon & Dennis Huisman & Erwin Abbink & Pieter-Jan Fioole & Matteo Fischetti & Gábor Maróti & Alexander Schrijver & Adri Steenbeek & Roelof Ybema, 2009. "The New Dutch Timetable: The OR Revolution," Interfaces, INFORMS, vol. 39(1), pages 6-17, February.
    3. Manish Verma & Vedat Verter & Michel Gendreau, 2011. "A Tactical Planning Model for Railroad Transportation of Dangerous Goods," Transportation Science, INFORMS, vol. 45(2), pages 163-174, May.
    4. Sato, Keisuke & Fukumura, Naoto, 2012. "Real-time freight locomotive rescheduling and uncovered train detection during disruption," European Journal of Operational Research, Elsevier, vol. 221(3), pages 636-648.
    5. Nielsen, Lars Kjær & Kroon, Leo & Maróti, Gábor, 2012. "A rolling horizon approach for disruption management of railway rolling stock," European Journal of Operational Research, Elsevier, vol. 220(2), pages 496-509.
    6. 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.
    7. Jean-François Cordeau & Paolo Toth & Daniele Vigo, 1998. "A Survey of Optimization Models for Train Routing and Scheduling," Transportation Science, INFORMS, vol. 32(4), pages 380-404, November.
    8. Khaled, Abdullah A. & Jin, Mingzhou & Clarke, David B. & Hoque, Mohammad A., 2015. "Train design and routing optimization for evaluating criticality of freight railroad infrastructures," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 71-84.
    9. Nejib Ben-Khedher & Josephine Kintanar & Cecile Queille & William Stripling, 1998. "Schedule Optimization at SNCF: From Conception to Day of Departure," Interfaces, INFORMS, vol. 28(1), pages 6-23, February.
    10. Lingaya, Norbert & Cordeau, Jean-Françcois & Desaulniers, Guy & Desrosiers, Jacques & Soumis, Françcois, 2002. "Operational car assignment at VIA Rail Canada," Transportation Research Part B: Methodological, Elsevier, vol. 36(9), pages 755-778, November.
    11. Cacchiani, Valentina & Toth, Paolo, 2012. "Nominal and robust train timetabling problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 727-737.
    12. Cynthia Barnhart & Hong Jin & Pamela H. Vance, 2000. "Railroad Blocking: A Network Design Application," Operations Research, INFORMS, vol. 48(4), pages 603-614, August.
    13. 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.
    14. Assad, Arjang A., 1980. "Modelling of rail networks: Toward a routing/makeup model," Transportation Research Part B: Methodological, Elsevier, vol. 14(1-2), pages 101-114.
    15. Dewilde, Thijs & Sels, Peter & Cattrysse, Dirk & Vansteenwegen, Pieter, 2014. "Improving the robustness in railway station areas," European Journal of Operational Research, Elsevier, vol. 235(1), pages 276-286.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. M. Shakibayifar & A. Sheikholeslami & F. Corman & E. Hassannayebi, 2020. "An integrated rescheduling model for minimizing train delays in the case of line blockage," Operational Research, Springer, vol. 20(1), pages 59-87, March.
    2. Maiyar, Lohithaksha M. & Thakkar, Jitesh J., 2019. "Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability," International Journal of Production Economics, Elsevier, vol. 217(C), pages 281-297.
    3. Ke, Ginger Y. & Verma, Manish, 2021. "A framework to managing disruption risk in rail-truck intermodal transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    4. Jabbarzadeh, Armin & Azad, Nader & Verma, Manish, 2020. "An optimization approach to planning rail hazmat shipments in the presence of random disruptions," Omega, Elsevier, vol. 96(C).
    5. Hosseini, S. Davod & Verma, Manish, 2018. "Conditional value-at-risk (CVaR) methodology to optimal train configuration and routing of rail hazmat shipments," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 79-103.
    6. Franciszek Restel & Łukasz Wolniewicz & Matea Mikulčić, 2021. "Method for Designing Robust and Energy Efficient Railway Schedules," Energies, MDPI, vol. 14(24), pages 1-12, December.
    7. Franciszek Restel & Szymon Mateusz Haładyn, 2022. "The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System," Energies, MDPI, vol. 15(17), pages 1-17, September.
    8. Fikar, Christian & Hirsch, Patrick & Posset, Martin & Gronalt, Manfred, 2016. "Impact of transalpine rail network disruptions: A study of the Brenner Pass," Journal of Transport Geography, Elsevier, vol. 54(C), pages 122-131.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vansteenwegen, Pieter & Dewilde, Thijs & Burggraeve, Sofie & Cattrysse, Dirk, 2016. "An iterative approach for reducing the impact of infrastructure maintenance on the performance of railway systems," European Journal of Operational Research, Elsevier, vol. 252(1), pages 39-53.
    2. Lee, Yusin & Lu, Li-Sin & Wu, Mei-Ling & Lin, Dung-Ying, 2017. "Balance of efficiency and robustness in passenger railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 142-156.
    3. Burggraeve, Sofie & Vansteenwegen, Pieter, 2017. "Robust routing and timetabling in complex railway stations," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 228-244.
    4. Polinder, Gert-Jaap & Breugem, Thomas & Dollevoet, Twan & Maróti, Gábor, 2019. "An adjustable robust optimization approach for periodic timetabling," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 50-68.
    5. Schön, Cornelia & König, Eva, 2018. "A stochastic dynamic programming approach for delay management of a single train line," European Journal of Operational Research, Elsevier, vol. 271(2), pages 501-518.
    6. Chen, Chongshuang & Dollevoet, Twan & Zhao, Jun, 2018. "One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 1-30.
    7. Chen, C. & Dollevoet, T.A.B. & Zhao, J., 2017. "One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm," Econometric Institute Research Papers EI-2017-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Taslimi, Bijan & Babaie Sarijaloo, Farnaz & Liu, Hongcheng & Pardalos, Panos M., 2022. "A novel mixed integer programming model for freight train travel time estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 676-688.
    9. Sels, P. & Dewilde, T. & Cattrysse, D. & Vansteenwegen, P., 2016. "Reducing the passenger travel time in practice by the automated construction of a robust railway timetable," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 124-156.
    10. Sparing, Daniel & Goverde, Rob M.P., 2017. "A cycle time optimization model for generating stable periodic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 198-223.
    11. Ke, Ginger Y. & Verma, Manish, 2021. "A framework to managing disruption risk in rail-truck intermodal transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    12. Jabbarzadeh, Armin & Azad, Nader & Verma, Manish, 2020. "An optimization approach to planning rail hazmat shipments in the presence of random disruptions," Omega, Elsevier, vol. 96(C).
    13. Lusby, Richard M. & Larsen, Jesper & Bull, Simon, 2018. "A survey on robustness in railway planning," European Journal of Operational Research, Elsevier, vol. 266(1), pages 1-15.
    14. Prashant Premkumar & P. N. Ram Kumar, 2019. "Literature Review of Locomotive Assignment Problem from Service Operations Perspective: The Case of Indian Railways," IIM Kozhikode Society & Management Review, , vol. 8(1), pages 74-86, January.
    15. Jiateng Yin & Lixing Yang & Xuesong Zhou & Tao Tang & Ziyou Gao, 2019. "Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(4), pages 297-320, June.
    16. Ruf, Moritz & Cordeau, Jean-François, 2021. "Adaptive large neighborhood search for integrated planning in railroad classification yards," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 26-51.
    17. Gábor Maróti, 2017. "A branch-and-bound approach for robust railway timetabling," Public Transport, Springer, vol. 9(1), pages 73-94, July.
    18. Nie, Wei & Li, Hao & Xiao, Na & Yang, Hao & Jiang, Zhishu & Buhigiro, Nsabimana, 2021. "Modeling and solving the last-shift period train scheduling problem in subway networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    19. M. D. Yap & N. Oort & R. Nes & B. Arem, 2018. "Identification and quantification of link vulnerability in multi-level public transport networks: a passenger perspective," Transportation, Springer, vol. 45(4), pages 1161-1180, July.
    20. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:85:y:2016:i:c:p:70-88. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.