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On offshore wind farm maintenance scheduling for decision support on vessel fleet composition

handle: 1956/22274
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.
- Wageningen University & Research Netherlands
- SINTEF AS Norway
- University of Malaga Spain
- University of Bergen Norway
- SINTEF AS Norway
Fleet composition, 000, 330, Scheduling, Heuristic, Offshore Wind Farm, 620, Maintenance planning, Scheduling; Offshore Wind Farm; Heuristic; Fleet composition; Maintenance planning
Fleet composition, 000, 330, Scheduling, Heuristic, Offshore Wind Farm, 620, Maintenance planning, Scheduling; Offshore Wind Farm; Heuristic; Fleet composition; Maintenance planning
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).34 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
