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Solving a wind turbine maintenance scheduling problem

Author

Listed:
  • Aurélien Froger

    (Université Bretagne Loire, Université Catholique de l’Ouest, LARIS EA 7315)

  • Michel Gendreau

    (CIRRELT Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, and Département de mathématiques et de génie industriel, Polytechnique Montréal)

  • Jorge E. Mendoza

    (Université François-Rabelais de Tours
    Centre de Recherches Mathématiques (UMI 3457 CNRS))

  • Eric Pinson

    (Université Bretagne Loire, Université Catholique de l’Ouest, LARIS EA 7315)

  • Louis-Martin Rousseau

    (CIRRELT Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, and Département de mathématiques et de génie industriel, Polytechnique Montréal)

Abstract

Driven by climate change mitigation efforts, the wind energy industry has significantly increased in recent years. In this context, it is essential to make its exploitation cost-effective. Maintenance of wind turbines therefore plays an essential role in reducing breakdowns and ensuring high productivity levels. In this paper, we discuss a challenging maintenance scheduling problem rising in the onshore wind power industry. While the research in the field primarily focuses on condition-based maintenance strategies, we aim to address the problem on a short-term horizon considering the wind speed forecast and a fine-grained resource management. The objective is to find a maintenance plan that maximizes the revenue from the electricity production of the turbines while taking into account multiple task execution modes and task-technician assignment constraints. To solve this problem, we propose a constraint programming-based large neighborhood search (CPLNS) approach. We also propose two integer linear programming formulations that we solve using a commercial solver. We report results on randomly generated instances built with input from wind forecasting and maintenance scheduling software companies. The CPLNS shows an average gap of 1.2% with respect to the optimal solutions if known, or to the best upper bounds otherwise. These computational results demonstrate the overall efficiency of the proposed metaheuristic.

Suggested Citation

  • Aurélien Froger & Michel Gendreau & Jorge E. Mendoza & Eric Pinson & Louis-Martin Rousseau, 2018. "Solving a wind turbine maintenance scheduling problem," Journal of Scheduling, Springer, vol. 21(1), pages 53-76, February.
    • Handle: RePEc:spr:jsched:v:21:y:2018:i:1:d:10.1007_s10951-017-0513-5
    DOI: 10.1007/s10951-017-0513-5
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    References listed on IDEAS

    as
    1. Gabriella Budai & Rommert Dekker & Robin P. Nicolai, 2008. "Maintenance and Production: A Review of Planning Models," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 13, pages 321-344, Springer.
    2. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    3. Malapert, Arnaud & Guéret, Christelle & Rousseau, Louis-Martin, 2012. "A constraint programming approach for a batch processing problem with non-identical job sizes," European Journal of Operational Research, Elsevier, vol. 221(3), pages 533-545.
    4. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    5. Rodriguez, Joaquín, 2007. "A constraint programming model for real-time train scheduling at junctions," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 231-245, February.
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    Cited by:

    1. Guastaroba, G. & Côté, J.-F. & Coelho, L.C., 2021. "The Multi-Period Workforce Scheduling and Routing Problem," Omega, Elsevier, vol. 102(C).
    2. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.

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