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A model and computational tool for crew scheduling in train transportation of mine materials by using a local search strategy

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  • Jorge Amaya

    (University of Chile)

  • Paula Uribe

    (University of Chile)

Abstract

This work introduces a model of the crew scheduling problem for the operation of trains in the mining industry in the North of Chile. The model possesses particular features due to specific regulations with which train operators in mine material transportation are required to comply: every week, a fixed set of trips must be made according to current demand for the transportation of mine products and supplies. In order to balance the loads of the crews in the long term, the proposed model generates an infinite horizon schedule by means of a rotative scheme in which each crew takes the place of the previous one at the beginning of the next week. This gives rise to a medium/large size 0–1 linear optimization problem, whose solution represents the optimal assignment of drivers to trips with the number of working hours per week distributed equally among crews. The model and algorithm have been implemented with a user interface suitable for the remote execution of real instances on a High Performance Computing platform. The transportation company regularly uses this computerized tool for planning crew schedules and generating efficient assignments for emerging and changing operational conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:topjnl:v:26:y:2018:i:3:d:10.1007_s11750-018-0479-2
    DOI: 10.1007/s11750-018-0479-2
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    References listed on IDEAS

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    1. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    2. Mesquita, Marta & Moz, Margarida & Paias, Ana & Pato, Margarida, 2015. "A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern," European Journal of Operational Research, Elsevier, vol. 245(2), pages 423-437.
    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. Martin Desrochers & François Soumis, 1989. "A Column Generation Approach to the Urban Transit Crew Scheduling Problem," Transportation Science, INFORMS, vol. 23(1), pages 1-13, February.
    5. A.T. Ernst & H. Jiang & M. Krishnamoorthy & H. Nott & D. Sier, 2001. "An Integrated Optimization Model for Train Crew Management," Annals of Operations Research, Springer, vol. 108(1), pages 211-224, November.
    6. Michel Gamache & François Soumis & Gérald Marquis & Jacques Desrosiers, 1999. "A Column Generation Approach for Large-Scale Aircrew Rostering Problems," Operations Research, INFORMS, vol. 47(2), pages 247-263, April.
    7. 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.
    8. 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.
    9. Nishi, Tatsushi & Sugiyama, Taichi & Inuiguchi, Masahiro, 2014. "Two-level decomposition algorithm for crew rostering problems with fair working condition," European Journal of Operational Research, Elsevier, vol. 237(2), pages 465-473.
    10. 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.
    11. 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.
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    Cited by:

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    2. 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.

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