IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v17y1983i1p4-31.html
   My bibliography  Save this article

A Matching Based Heuristic for Scheduling Mass Transit Crews and Vehicles

Author

Listed:
  • Michael Ball

    (College of Business and Management, University of Maryland, College Park, Maryland)

  • Lawrence Bodin

    (College of Business and Management, University of Maryland, College Park, Maryland)

  • Robert Dial

    (Urban Mass Transportation Administration, United States Department of Transportation, Washington, D.C.)

Abstract

In this paper, we describe a computerized procedure for scheduling mass transit crews and vehicles. The procedure differs from previous methods in that rather than first scheduling vehicles and then crews, it schedules vehicles and crews simultaneously. Several of the subproblems are solved as matching problems on graphs. A computational test of this procedure using a database from the Baltimore Metropolitan Transit Authority has produced solutions of lower cost than those used by the transit authority.

Suggested Citation

  • Michael Ball & Lawrence Bodin & Robert Dial, 1983. "A Matching Based Heuristic for Scheduling Mass Transit Crews and Vehicles," Transportation Science, INFORMS, vol. 17(1), pages 4-31, February.
    • Handle: RePEc:inm:ortrsc:v:17:y:1983:i:1:p:4-31
    DOI: 10.1287/trsc.17.1.4
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/trsc.17.1.4
    Download Restriction: no
    File URL: https://libkey.io/10.1287/trsc.17.1.4?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
    ---><---

    Citations

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


    Cited by:

    1. Markó Horváth & Tamás Kis, 2019. "Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(1), pages 39-67, March.
    2. Ingmar Steinzen & Vitali Gintner & Leena Suhl & Natalia Kliewer, 2010. "A Time-Space Network Approach for the Integrated Vehicle- and Crew-Scheduling Problem with Multiple Depots," Transportation Science, INFORMS, vol. 44(3), pages 367-382, August.
    3. Friberg, Christian & Haase, Knut, 1996. "An exact algorithm for the vehicle and crew scheduling problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 416, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    4. Rodrigues, Maikol M. & de Souza, Cid C. & Moura, Arnaldo V., 2006. "Vehicle and crew scheduling for urban bus lines," European Journal of Operational Research, Elsevier, vol. 170(3), pages 844-862, May.
    5. Beasley, J. E. & Cao, B., 1996. "A tree search algorithm for the crew scheduling problem," European Journal of Operational Research, Elsevier, vol. 94(3), pages 517-526, November.
    6. Chu, Sydney C.K., 2007. "Generating, scheduling and rostering of shift crew-duties: Applications at the Hong Kong International Airport," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1764-1778, March.
    7. Medaglia, Andres L. & Fang, Shu-Cherng, 2003. "A genetic-based framework for solving (multi-criteria) weighted matching problems," European Journal of Operational Research, Elsevier, vol. 149(1), pages 77-101, August.
    8. Perumal, S.S.G. & Dollevoet, T.A.B. & Huisman, D. & Lusby, R.M. & Larsen, J. & Riis, M., 2020. "Solution Approaches for Vehicle and Crew Scheduling with Electric Buses," Econometric Institute Research Papers EI-2020-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Niu, Huimin & Zhou, Xuesong & Tian, Xiaopeng, 2018. "Coordinating assignment and routing decisions in transit vehicle schedules: A variable-splitting Lagrangian decomposition approach for solution symmetry breaking," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 70-101.
    10. Elena Fernández & Oscar Meza, 2004. "Even Cycles and Perfect Matching Problems with Side Constraints," Journal of Combinatorial Optimization, Springer, vol. 8(3), pages 381-396, September.
    11. Chew, Joanne S.C. & Zhang, Lele & Gan, Heng S., 2019. "Optimizing limited-stop services with vehicle assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 228-246.
    12. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    13. Jing-Quan Li, 2014. "Transit Bus Scheduling with Limited Energy," Transportation Science, INFORMS, vol. 48(4), pages 521-539, November.
    14. Knut Haase & Guy Desaulniers & Jacques Desrosiers, 2001. "Simultaneous Vehicle and Crew Scheduling in Urban Mass Transit Systems," Transportation Science, INFORMS, vol. 35(3), pages 286-303, August.
    15. Haghani, Ali & Banihashemi, Mohamadreza, 2002. "Heuristic approaches for solving large-scale bus transit vehicle scheduling problem with route time constraints," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(4), pages 309-333, May.
    16. A. Mingozzi & M. A. Boschetti & S. Ricciardelli & L. Bianco, 1999. "A Set Partitioning Approach to the Crew Scheduling Problem," Operations Research, INFORMS, vol. 47(6), pages 873-888, December.
    17. Shen, Yindong & Xu, Jia & Li, Jingpeng, 2016. "A probabilistic model for vehicle scheduling based on stochastic trip times," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 19-31.
    18. William Cook & André Rohe, 1999. "Computing Minimum-Weight Perfect Matchings," INFORMS Journal on Computing, INFORMS, vol. 11(2), pages 138-148, May.
    19. Perumal, Shyam S.G. & Larsen, Jesper & Lusby, Richard M. & Riis, Morten & Sørensen, Kasper S., 2019. "A matheuristic for the driver scheduling problem with staff cars," European Journal of Operational Research, Elsevier, vol. 275(1), pages 280-294.
    20. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.

    More about this item

    Statistics

    Access and download statistics

    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:ortrsc:v:17:y:1983:i:1:p:4-31. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.