IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v48y2018i5p436-448.html
   My bibliography  Save this article

Crew Decision Assist: System for Optimizing Crew Assignments at BNSF Railway

Author

Listed:
  • Brian Roth

    (BNSF Railway, Fort Worth, Texas 76131)

  • Anantaram Balakrishnan

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78705)

  • Pooja Dewan

    (BNSF Railway, Fort Worth, Texas 76131)

  • April Kuo

    (BNSF Railway, Fort Worth, Texas 76131)

  • Dasaradh Mallampati

    (BNSF Railway, Fort Worth, Texas 76131)

  • Juan Morales

    (BNSF Railway, Fort Worth, Texas 76131)

Abstract

Rail is the preferred mode of transport for many categories of freight because of its low cost and energy efficiency. Rail accounts for approximately 40%, measured in ton-miles, of all freight movements in the United States. To maintain their competitive advantage and effectively utilize their large investments in rail infrastructure, freight railroad companies place considerable emphasis on improving the cost efficiency of their operations. Crew costs, including payments to crew members and expenses for crew repositioning and lodging at stations away from the home base, constitute a significant portion of railroad operating expenses. This paper describes the development of an optimization model and solution method and the implementation of a system called “crew decision assist” to support crew scheduling at BNSF Railway. The work was motivated by the company’s desire to replace its current manual crew-planning process with a systematic and effective approach. Preexisting crew-scheduling models did not adequately capture all the options and constraints that arise in practice, such as the option to use extra crew members or policies to jointly reposition engineers and conductors. We, therefore, developed a tailored model and solution approach that incorporates various practical features and requirements for crew assignment at BNSF and accounts for uncertainty in train schedules. Our decision support system, based on this method, interfaces with existing information systems to retrieve the necessary data and quickly generate effective crew-deployment plans when train schedules change. The system was recently introduced for use by crew planners at BNSF and has already reduced crew costs, yielding estimated annual savings of several million dollars.

Suggested Citation

  • Brian Roth & Anantaram Balakrishnan & Pooja Dewan & April Kuo & Dasaradh Mallampati & Juan Morales, 2018. "Crew Decision Assist: System for Optimizing Crew Assignments at BNSF Railway," Interfaces, INFORMS, vol. 48(5), pages 436-448, October.
  • Handle: RePEc:inm:orinte:v:48:y:2018:i:5:p:436-448
    DOI: 10.1287/inte.2018.0963
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2018.0963
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.2018.0963?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
    ---><---

    References listed on IDEAS

    as
    1. Alberto Caprara & Paolo Toth & Daniele Vigo & Matteo Fischetti, 1998. "Modeling and Solving the Crew Rostering Problem," Operations Research, INFORMS, vol. 46(6), pages 820-830, December.
    2. Andrew J. Schaefer & Ellis L. Johnson & Anton J. Kleywegt & George L. Nemhauser, 2005. "Airline Crew Scheduling Under Uncertainty," Transportation Science, INFORMS, vol. 39(3), pages 340-348, August.
    3. Silke Jütte & Marc Albers & Ulrich W. Thonemann & Knut Haase, 2011. "Optimizing Railway Crew Scheduling at DB Schenker," Interfaces, INFORMS, vol. 41(2), pages 109-122, April.
    4. Balaji Gopalakrishnan & Ellis. Johnson, 2005. "Airline Crew Scheduling: State-of-the-Art," Annals of Operations Research, Springer, vol. 140(1), pages 305-337, November.
    5. Anantaram Balakrishnan & April Kuo & Xiaoyan Si, 2016. "Real-Time Decision Support for Crew Assignment in Double-Ended Districts for U.S. Freight Railways," Transportation Science, INFORMS, vol. 50(4), pages 1337-1359, November.
    6. Şahin, Güvenç & Yüceoğlu, Birol, 2011. "Tactical crew planning in railways," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1221-1243.
    7. Erwin Abbink & Matteo Fischetti & Leo Kroon & Gerrit Timmer & Michiel Vromans, 2005. "Reinventing Crew Scheduling at Netherlands Railways," Interfaces, INFORMS, vol. 35(5), pages 393-401, October.
    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. Guo, Jia & Bard, Jonathan F., 2024. "Weekly scheduling for freight rail engineers & trainmen," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    2. Douglas S. Altner & Erica K. Mason & Les D. Servi, 2019. "Two-stage stochastic days-off scheduling of multi-skilled analysts with training options," Journal of Combinatorial Optimization, Springer, vol. 38(1), pages 111-129, July.
    3. Sobrie, Léon & Verschelde, Marijn & Roets, Bart, 2024. "Explainable real-time predictive analytics on employee workload in digital railway control rooms," European Journal of Operational Research, Elsevier, vol. 317(2), pages 437-448.

    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. Heil, Julia & Hoffmann, Kirsten & Buscher, Udo, 2020. "Railway crew scheduling: Models, methods and applications," European Journal of Operational Research, Elsevier, vol. 283(2), pages 405-425.
    2. Silke Jütte & Daniel Müller & Ulrich W. Thonemann, 2017. "Optimizing railway crew schedules with fairness preferences," Journal of Scheduling, Springer, vol. 20(1), pages 43-55, February.
    3. Fuentes, Manuel & Cadarso, Luis & Marín, Ángel, 2019. "A hybrid model for crew scheduling in rail rapid transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 248-265.
    4. Philippe Racette & Frédéric Quesnel & Andrea Lodi & François Soumis, 2024. "Gaining insight into crew rostering instances through ML-based sequential assignment," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 537-578, October.
    5. Silke Jütte & Marc Albers & Ulrich W. Thonemann & Knut Haase, 2011. "Optimizing Railway Crew Scheduling at DB Schenker," Interfaces, INFORMS, vol. 41(2), pages 109-122, April.
    6. Kirsten Hoffmann & Udo Buscher & Janis Sebastian Neufeld & Felix Tamke, 2017. "Solving Practical Railway Crew Scheduling Problems with Attendance Rates," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(3), pages 147-159, June.
    7. Neufeld, Janis S. & Scheffler, Martin & Tamke, Felix & Hoffmann, Kirsten & Buscher, Udo, 2021. "An efficient column generation approach for practical railway crew scheduling with attendance rates," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1113-1130.
    8. 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.
    9. Suyabatmaz, Ali Çetin & Şahin, Güvenç, 2015. "Railway crew capacity planning problem with connectivity of schedules," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 84(C), pages 88-100.
    10. Feng, Tao & Lusby, Richard M. & Zhang, Yongxiang & Tao, Siyu & Zhang, Bojian & Peng, Qiyuan, 2024. "A branch-and-price algorithm for integrating urban rail crew scheduling and rostering problems," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    11. Jesica Armas & Luis Cadarso & Angel A. Juan & Javier Faulin, 2017. "A multi-start randomized heuristic for real-life crew rostering problems in airlines with work-balancing goals," Annals of Operations Research, Springer, vol. 258(2), pages 825-848, November.
    12. Masoud Yaghini & Mohammad Karimi & Mohadeseh Rahbar, 2015. "A set covering approach for multi-depot train driver scheduling," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 636-654, April.
    13. Wolbeck, Lena Antonia, 2019. "Fairness aspects in personnel scheduling," Discussion Papers 2019/16, Free University Berlin, School of Business & Economics.
    14. Jifan Zhang & Salih Tutun & Samira Fazel Anvaryazdi & Mohammadhossein Amini & Durai Sundaramoorthi & Hema Sundaramoorthi, 2024. "Management of resource sharing in emergency response using data-driven analytics," Annals of Operations Research, Springer, vol. 339(1), pages 663-692, August.
    15. Abdelouahab Zaghrouti & Issmail El Hallaoui & François Soumis, 2020. "Improving set partitioning problem solutions by zooming around an improving direction," Annals of Operations Research, Springer, vol. 284(2), pages 645-671, January.
    16. Atoosa Kasirzadeh & Mohammed Saddoune & François Soumis, 2017. "Airline crew scheduling: models, algorithms, and data sets," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(2), pages 111-137, June.
    17. Vahid Zeighami & François Soumis, 2019. "Combining Benders’ Decomposition and Column Generation for Integrated Crew Pairing and Personalized Crew Assignment Problems," Transportation Science, INFORMS, vol. 53(5), pages 1479-1499, September.
    18. Mohamed Haouari & Farah Zeghal Mansour & Hanif D. Sherali, 2019. "A New Compact Formulation for the Daily Crew Pairing Problem," Transportation Science, INFORMS, vol. 53(3), pages 811-828, May.
    19. Hartog, A. & Huisman, D. & Abbink, E.J.W. & Kroon, L.G., 2006. "Decision support for crew rostering at NS," Econometric Institute Research Papers EI 2006-04, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Scheffler, Martin & Neufeld, Janis S. & Hölscher, Michael, 2020. "An MIP-based heuristic solution approach for the locomotive assignment problem focussing on (dis-)connecting processes," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 64-80.

    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:inm:orinte:v:48:y:2018:i:5:p:436-448. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.