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Hybrid Column Generation Approaches for Urban Transit Crew Management Problems

Author

Listed:
  • Tallys H. Yunes

    (Institute of Computing, University of Campinas, Caixa Postal 6176, CEP 13084-971, Campinas, SP, Brazil)

  • Arnaldo V. Moura

    (Institute of Computing, University of Campinas, Caixa Postal 6176, CEP 13084-971, Campinas, SP, Brazil)

  • Cid C. de Souza

    (Institute of Computing, University of Campinas, Caixa Postal 6176, CEP 13084-971, Campinas, SP, Brazil)

Abstract

This article considers the overall crew management problem arising from the daily operation of an urban transit bus company that serves the metropolitan area of the city of Belo Horizonte, Brazil. Due to its intrinsic complexity, the problem is divided in two distinct subproblems: crew scheduling and crew rostering . We have investigated each of these problems using mathematical programming (MP) and constraint logic programming (CLP) approaches. In addition, we developed hybrid column generation algorithms for solving these problems, combining MP and CLP. The hybrid algorithms always performed better, when obtaining optimal solutions, than the two previous isolated approaches. In particular, they proved to be much faster for the scheduling problem. All the proposed algorithms have been implemented and tested over real-world data obtained from the aforementioned company. The coefficient matrix of the linear program associated with some instances of the scheduling problem contains tens of millions of columns; this number is even larger for the rostering problem. The analysis of our experiments indicates that it was possible to find high-quality, and many times optimal, solutions that were suitable for the company’s needs. These solutions were obtained within reasonable computational times on a desktop PC.

Suggested Citation

  • Tallys H. Yunes & Arnaldo V. Moura & Cid C. de Souza, 2005. "Hybrid Column Generation Approaches for Urban Transit Crew Management Problems," Transportation Science, INFORMS, vol. 39(2), pages 273-288, May.
    • Handle: RePEc:inm:ortrsc:v:39:y:2005:i:2:p:273-288
    DOI: 10.1287/trsc.1030.0078
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    References listed on IDEAS

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    1. Jens O. Brunner & Jonathan F. Bard & Jan M. Köhler, 2013. "Bounded flexibility in days‐on and days‐off scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(8), pages 678-701, December.
    2. Cortés, Cristián E. & Gendreau, Michel & Rousseau, Louis Martin & Souyris, Sebastián & Weintraub, Andrés, 2014. "Branch-and-price and constraint programming for solving a real-life technician dispatching problem," European Journal of Operational Research, Elsevier, vol. 238(1), pages 300-312.
    3. Stefano Gualandi & Federico Malucelli, 2012. "Exact Solution of Graph Coloring Problems via Constraint Programming and Column Generation," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 81-100, February.
    4. Paraskevopoulos, Dimitris C. & Laporte, Gilbert & Repoussis, Panagiotis P. & Tarantilis, Christos D., 2017. "Resource constrained routing and scheduling: Review and research prospects," European Journal of Operational Research, Elsevier, vol. 263(3), pages 737-754.
    5. Shyam S. G. Perumal & Jesper Larsen & Richard M. Lusby & Morten Riis & Tue R. L. Christensen, 2022. "A column generation approach for the driver scheduling problem with staff cars," Public Transport, Springer, vol. 14(3), pages 705-738, October.
    6. Emir Hüseyin Özder & Evrencan Özcan & Tamer Eren, 2019. "Staff Task-Based Shift Scheduling Solution with an ANP and Goal Programming Method in a Natural Gas Combined Cycle Power Plant," Mathematics, MDPI, vol. 7(2), pages 1-26, February.
    7. Michela Milano & Mark Wallace, 2010. "Integrating Operations Research in Constraint Programming," Annals of Operations Research, Springer, vol. 175(1), pages 37-76, March.
    8. Attila Tóth & Miklós Krész, 2013. "An efficient solution approach for real-world driver scheduling problems in urban bus transportation," 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. 21(1), pages 75-94, June.
    9. Restrepo, María I. & Lozano, Leonardo & Medaglia, Andrés L., 2012. "Constrained network-based column generation for the multi-activity shift scheduling problem," International Journal of Production Economics, Elsevier, vol. 140(1), pages 466-472.
    10. 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.
    11. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    12. Lin Xie & Marius Merschformann & Natalia Kliewer & Leena Suhl, 2017. "Metaheuristics approach for solving personalized crew rostering problem in public bus transit," Journal of Heuristics, Springer, vol. 23(5), pages 321-347, October.
    13. Anthony Han & Elvis Li, 2014. "A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system," Annals of Operations Research, Springer, vol. 223(1), pages 173-193, December.
    14. Stefano Gualandi & Federico Malucelli, 2013. "Constraint Programming-based Column Generation," Annals of Operations Research, Springer, vol. 204(1), pages 11-32, April.
    15. Tallys Yunes & Ionuţ D. Aron & J. N. Hooker, 2010. "An Integrated Solver for Optimization Problems," Operations Research, INFORMS, vol. 58(2), pages 342-356, April.
    16. Jorge Amaya & Paula Uribe, 2018. "A model and computational tool for crew scheduling in train transportation of mine materials by using a local search strategy," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 383-402, October.

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