This paper addresses the maintenance planning for rotor blades of onshore wind turbines at a tactical level, motivated by the maintenance department of a German onshore wind turbine manufacturer. The goal is to select maintenance teams from external service providers and assign preventive maintenance tasks to them for one season while minimizing the total costs. The operational scheduling of the tasks and routing of the teams are anticipated to determine the capacity demand. The depots, working time regulations, qualifications, and cost rates are team-specific. We describe the decision process of the tactical planning problem and formulate the novel mathematical decision model solved within the process. A Variable Neighborhood Search with Adaptive Local Search (VNSALS) with a problem-specific perturbation is introduced as a heuristic solution approach. Using real-world data from the turbine manufacturer’s maintenance department, we conduct numerical studies to evaluate the performance of our proposed solution approach compared to the commercial solver Gurobi. The results show that our heuristic outperforms the solver regarding solution quality and computation time. Additionally, we give managerial insights into the structural properties of the model. • Literature review on maintenance planning for wind turbines. • Novel MILP model formulation for tactical maintenance planning for onshore wind turbines. • Adjustment of problem-specific variable neighborhood search as a metaheuristic. • Heuristic outperforms Gurobi on real dataset from industrial partner. • Managerial insights into the structural properties of the model.
Klingebiel et al. (Wed,) studied this question.