Journal of Scheduling

, Volume 21, Issue 1, pp 53–76 | Cite as

Solving a wind turbine maintenance scheduling problem

  • Aurélien FrogerEmail author
  • Michel Gendreau
  • Jorge E. Mendoza
  • Eric Pinson
  • Louis-Martin Rousseau


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.


Maintenance Scheduling Large neighborhood search Constraint programming 



The authors would like to kindly thank two anonymous reviewers for their comments and suggestions. This work was supported by Angers Loire Métropole through its research grant program; and by the Natural Sciences and Engineering Research Council of Canada (NSERC) through a grant that enabled the collaboration with the Canadian company WPred, which we would like to thank for their expertise.


  1. Baptiste, P., Le Pape, C., & Nuijten, W. (2001). Constraint-based scheduling: Applying constraint programming to scheduling problems. Dordrecht: Kluwer.CrossRefGoogle Scholar
  2. Budai, G., Dekker, R., & Nicolai, R. (2008). Maintenance and production: A review of planning models. Complex system maintenance handbook, Springer series in reliability engineering (pp. 321–324). London: Springer.Google Scholar
  3. Cordeau, J.-F., Laporte, G., Pasin, F., & Ropke, S. (2010). Scheduling technicians and tasks in a telecommunications company. Journal of Scheduling, 13(4), 393–409.CrossRefGoogle Scholar
  4. De Reyck, B., Demeulemeester, E., & Herroelen, W. (1998). Local search methods for the discrete time/resource trade-off problem in project networks. Naval Research Logistics (NRL), 55(6), 553–578.CrossRefGoogle Scholar
  5. Ding, F., Tian, Z., & Jin, T. (2013). Maintenance modeling and optimization for wind turbine systems: A review. In 2013 International conference on quality, reliability, risk, maintenance, and safety engineering (QR2MSE), pp. 569–575.Google Scholar
  6. Froger, A., Gendreau, M., Mendoza, J., Pinson, E., & Rousseau, L.-M. (2016). Maintenance scheduling in the electricity industry: A literature review. European Journal of Operational Research, 251(3), 695–706.Google Scholar
  7. Kovács, A., Erds, G., Viharos, Z., & Monostori, L. (2011). A system for the detailed scheduling of wind farm maintenance. CIRP Annals - Manufacturing Technology, 60(1), 497–501.Google Scholar
  8. Malapert, A., Guéret, C., & Rousseau, L.-M. (2012). A constraint programming approach for a batch processing problem with non-identical job sizes. European Journal of Operational Research, 221(3), 533–545.CrossRefGoogle Scholar
  9. Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers and Operations Research, 34(8), 2403–2435.CrossRefGoogle Scholar
  10. Pisinger, D., & Ropke, S. (2010). Large Neighborhood Search. In M. Gendreau & J.-Y. Potvin (Eds.), Handbook of metaheuristics (Vol. 146, pp. 399–419)., International series in operations research & management science New York: Springer.CrossRefGoogle Scholar
  11. Prud’homme, C., Fages, J.-G., & Lorca, X. (2014). Choco3 documentation. TASC, INRIA Rennes, LINA CNRS UMR 6241, COSLING S.A.S.Google Scholar
  12. Rodriguez, J. (2007). A constraint programming model for real-time train scheduling at junctions. Transportation Research Part B: Methodological, 41(2), 231–245.CrossRefGoogle Scholar
  13. Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455–472.Google Scholar
  14. Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. Principles and Practice of Constraint Programming-CP, 1998, 417–431.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Aurélien Froger
    • 1
    Email author
  • Michel Gendreau
    • 2
  • Jorge E. Mendoza
    • 3
    • 4
  • Eric Pinson
    • 1
  • Louis-Martin Rousseau
    • 2
  1. 1.Université Bretagne Loire, Université Catholique de l’Ouest, LARIS EA 7315AngersFrance
  2. 2.CIRRELT Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, and Département de mathématiques et de génie industriel, Polytechnique MontréalMontrealCanada
  3. 3.CNRS, LI EA 6300, ROOT ERL CNRS 6305Université François-Rabelais de ToursToursFrance
  4. 4.Centre de Recherches Mathématiques (UMI 3457 CNRS)MontrealCanada

Personalised recommendations