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A solution approach for dynamic vehicle and crew scheduling

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  • Huisman, D.
  • Wagelmans, A.P.M.

Abstract

In this paper, we discuss the dynamic vehicle and crew scheduling problem and we propose a solution approach consisting of solving a sequence of optimization problems. Furthermore, we explain why it is useful to consider such a dynamic approach and compare it with a static one. Moreover, we perform a sensitivity analysis on our main assumption that the travel times of the trips are known exactly a certain amount of time before actual operation. We provide extensive computational results on some real-world data instances of a large public transport company in the Netherlands. Due to the complexity of the vehicle and crew scheduling problem, we solve only small and medium-sized instances with such a dynamic approach. We show that the results are good in the case of a single depot. However, in the multiple-depot case, the dynamic approach does not perform so well. We investigate why this is the case and conclude that the fact that the instance has to be split in several smaller ones, has a negative effect on the performance.

Suggested Citation

  • Huisman, D. & Wagelmans, A.P.M., 2004. "A solution approach for dynamic vehicle and crew scheduling," Econometric Institute Research Papers EI 2004-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1182
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    1. Huisman, D. & Freling, R. & Wagelmans, A.P.M., 2003. "Multiple-Depot Integrated Vehicle and Crew Scheduling," Econometric Institute Research Papers EI 2003-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Huisman, Dennis & Wagelmans, Albert P.M., 2006. "A solution approach for dynamic vehicle and crew scheduling," European Journal of Operational Research, Elsevier, vol. 172(2), pages 453-471, July.
    3. Mirela Stojkovi'{c} & François Soumis, 2001. "An Optimization Model for the Simultaneous Operational Flight and Pilot Scheduling Problem," Management Science, INFORMS, vol. 47(9), pages 1290-1305, September.
    4. Freling, R. & Huisman, D. & Wagelmans, A.P.M., 2000. "Models and algorithms for Integration of Vehicle and Crew Scheduling," ERIM Report Series Research in Management ERS-2000-14-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. 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.
    6. Huisman, D. & Freling, R. & Wagelmans, A.P.M., 2001. "A dynamic approach to vehicle scheduling," ERIM Report Series Research in Management ERS-2001-35-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Joyce W. Yen & John R. Birge, 2006. "A Stochastic Programming Approach to the Airline Crew Scheduling Problem," Transportation Science, INFORMS, vol. 40(1), pages 3-14, February.
    8. Dennis Huisman & Richard Freling & Albert P. M. Wagelmans, 2004. "A Robust Solution Approach to the Dynamic Vehicle Scheduling Problem," Transportation Science, INFORMS, vol. 38(4), pages 447-458, November.
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    Cited by:

    1. Wu, Weitiao & Lin, Yue & Liu, Ronghui & Jin, Wenzhou, 2022. "The multi-depot electric vehicle scheduling problem with power grid characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 322-347.
    2. Huisman, Dennis & Wagelmans, Albert P.M., 2006. "A solution approach for dynamic vehicle and crew scheduling," European Journal of Operational Research, Elsevier, vol. 172(2), pages 453-471, July.
    3. 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.
    4. Bastian Amberg & Boris Amberg & Natalia Kliewer, 2019. "Robust Efficiency in Urban Public Transportation: Minimizing Delay Propagation in Cost-Efficient Bus and Driver Schedules," Service Science, INFORMS, vol. 53(1), pages 89-112, February.
    5. Dauzère-Pérès, Stéphane & De Almeida, David & Guyon, Olivier & Benhizia, Faten, 2015. "A Lagrangian heuristic framework for a real-life integrated planning problem of railway transportation resources," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 138-150.
    6. Gabriel E. Sánchez-Martínez & Nigel H. M. Wilson & Haris N. Koutsopoulos, 2017. "Schedule-free high-frequency transit operations," Public Transport, Springer, vol. 9(1), pages 285-305, July.
    7. Balázs Dávid & Miklós Krész, 2017. "The dynamic vehicle rescheduling problem," 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. 25(4), pages 809-830, December.
    8. Jütte, Silke & Thonemann, Ulrich W., 2012. "Divide-and-price: A decomposition algorithm for solving large railway crew scheduling problems," European Journal of Operational Research, Elsevier, vol. 219(2), pages 214-223.
    9. 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.
    10. Federico Malucelli & Emanuele Tresoldi, 2019. "Delay and disruption management in local public transportation via real-time vehicle and crew re-scheduling: a case study," Public Transport, Springer, vol. 11(1), pages 1-25, June.

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