TY - JOUR
T1 - On offshore wind farm maintenance scheduling for decision support on vessel fleet composition
AU - Gutierrez-Alcoba, A.
AU - Hendrix, E.M.T.
AU - Ortega, G.
AU - Halvorsen-Weare, E.E.
AU - Haugland, D.
PY - 2019/11/16
Y1 - 2019/11/16
N2 - Maintenance costs account for a large part of the total cost of an offshore wind farm. Several models have been presented in the literature to optimize the fleet composition of the required vessels to support maintenance tasks. We provide a mixed integer linear programming (MILP) description of such a model, where on the higher level, the fleet composition is decided and on the lower level the maintenance operations are scheduled for a set of weather and breakdown scenarios. A drawback of deciding an a priori information schedule for the coming year is that, the weather outcomes and breakdowns are not known in advance. Consequently, given a fleet composition, its corresponding maintenance costs are underestimated compared to what can be realised in practice under incomplete information. Therefore, we present a heuristic that simulates the practical scheduling and may provide a better cost estimate. The latter method is used to evaluate a fleet composition based on available information and it is compared with the MILP solution based on a priori information.
AB - Maintenance costs account for a large part of the total cost of an offshore wind farm. Several models have been presented in the literature to optimize the fleet composition of the required vessels to support maintenance tasks. We provide a mixed integer linear programming (MILP) description of such a model, where on the higher level, the fleet composition is decided and on the lower level the maintenance operations are scheduled for a set of weather and breakdown scenarios. A drawback of deciding an a priori information schedule for the coming year is that, the weather outcomes and breakdowns are not known in advance. Consequently, given a fleet composition, its corresponding maintenance costs are underestimated compared to what can be realised in practice under incomplete information. Therefore, we present a heuristic that simulates the practical scheduling and may provide a better cost estimate. The latter method is used to evaluate a fleet composition based on available information and it is compared with the MILP solution based on a priori information.
UR - https://doi.org/10.1016/j.ejor.2019.04.020
U2 - 10.1016/j.ejor.2019.04.020
DO - 10.1016/j.ejor.2019.04.020
M3 - Article
SN - 0377-2217
JO - European Journal of Operational Research
JF - European Journal of Operational Research
ER -