IDEAS home Printed from https://ideas.repec.org/a/spr/jsched/v21y2018i1d10.1007_s10951-017-0513-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10951-017-0513-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10951-017-0513-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Woller & Jakub Rada & Miroslav Kulich, 2023. "The ALNS metaheuristic for the transmission maintenance scheduling," Journal of Heuristics, Springer, vol. 29(2), pages 349-382, June.
    2. JANSSENS, Jochen & DE CORTE, Annelies & SÖRENSEN, Kenneth, 2016. "Water distribution network design optimisation with respect to reliability," Working Papers 2016007, University of Antwerp, Faculty of Business and Economics.
    3. Bach, Lukas & Hasle, Geir & Schulz, Christian, 2019. "Adaptive Large Neighborhood Search on the Graphics Processing Unit," European Journal of Operational Research, Elsevier, vol. 275(1), pages 53-66.
    4. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    5. Martins, Sara & Ostermeier, Manuel & Amorim, Pedro & Hübner, Alexander & Almada-Lobo, Bernardo, 2019. "Product-oriented time window assignment for a multi-compartment vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 276(3), pages 893-909.
    6. Dessouky, Maged M & Shao, Yihuan E, 2017. "Routing Strategies for Efficient Deployment of Alternative Fuel Vehicles for Freight Delivery," Institute of Transportation Studies, Working Paper Series qt0nj024qn, Institute of Transportation Studies, UC Davis.
    7. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    8. Gokturk Poyrazoglu & HyungSeon Oh, 2019. "Co-optimization of Transmission Maintenance Scheduling and Production Cost Minimization," Energies, MDPI, vol. 12(15), pages 1-18, July.
    9. SteadieSeifi, M. & Dellaert, N.P. & Nuijten, W. & Van Woensel, T., 2017. "A metaheuristic for the multimodal network flow problem with product quality preservation and empty repositioning," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 321-344.
    10. repec:dar:wpaper:62383 is not listed on IDEAS
    11. Parvez Farazi, Nahid & Zou, Bo & Tulabandhula, Theja, 2022. "Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    12. He, Dongdong & Guan, Wei, 2023. "Promoting service quality with incentive contracts in rural bus integrated passenger-freight service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    13. Nascimento, Paulo Jorge & Silva, Cristóvão & Antunes, Carlos Henggeler & Moniz, Samuel, 2024. "Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems," European Journal of Operational Research, Elsevier, vol. 317(1), pages 92-110.
    14. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    15. Thomas Bittar & Pierre Carpentier & Jean-Philippe Chancelier & Jérôme Lonchampt, 2022. "A decomposition method by interaction prediction for the optimization of maintenance scheduling," Annals of Operations Research, Springer, vol. 316(1), pages 229-267, September.
    16. Andrea D'Ariano & Francesco Corman & Dario Pacciarelli & Marco Pranzo, 2008. "Reordering and Local Rerouting Strategies to Manage Train Traffic in Real Time," Transportation Science, INFORMS, vol. 42(4), pages 405-419, November.
    17. Jone R. Hansen & Kjetil Fagerholt & Magnus Stålhane & Jørgen G. Rakke, 2020. "An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships," Journal of Heuristics, Springer, vol. 26(6), pages 885-912, December.
    18. Arda, Yasemin & Cattaruzza, Diego & François, Véronique & Ogier, Maxime, 2024. "Home chemotherapy delivery: An integrated production scheduling and multi-trip vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 317(2), pages 468-486.
    19. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    20. J. Álvaro Gómez-Pantoja & M. Angélica Salazar-Aguilar & José Luis González-Velarde, 2021. "The food bank resource allocation problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 266-286, April.
    21. Li, Yuan & Chen, Haoxun & Prins, Christian, 2016. "Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests," European Journal of Operational Research, Elsevier, vol. 252(1), pages 27-38.

    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:spr:jsched:v:21:y:2018:i:1:d:10.1007_s10951-017-0513-5. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.