Solving a wind turbine maintenance scheduling problem

Aurélien Froger (), Michel Gendreau (), Jorge E. Mendoza (), Eric Pinson () and Louis-Martin Rousseau ()
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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
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

Journal of Scheduling, 2018, vol. 21, issue 1, No 5, 53-76

Abstract: 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.

Keywords: Maintenance; Scheduling; Large neighborhood search; Constraint programming (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10951-017-0513-5

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