Solving a wind turbine maintenance scheduling problem

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 product...

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Bibliographic Details
Published in:Journal of scheduling 2018-02, Vol.21 (1), p.53-76
Main Authors: Froger, Aurélien, Gendreau, Michel, Mendoza, Jorge E., Pinson, Eric, Rousseau, Louis-Martin
Format: Article
Language:English
Subjects:
Artificial Intelligence
Business and Management
Calculus of Variations and Optimal Control
Optimization
Computer Science
Electric industries
Formulations
Heuristic methods
Integer programming
Linear programming
Maintenance management
Operations Research
Operations Research/Decision Theory
Optimization
Production scheduling
Resource management
Scheduling
Supply Chain Management
Upper bounds
Wind power
Wind speed
Wind turbines
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Summary: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.
ISSN:1094-6136
1099-1425
DOI:10.1007/s10951-017-0513-5