Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 559444
Title On offshore wind farm maintenance scheduling for decision support on vessel fleet composition
Author(s) Alcoba, A.G.; Hendrix, E.M.T.; Ortega, G.; Halvorsen-Waere, E.E.; Haugland, D.
Source European Journal of Operational Research 279 (2019)1. - p. 124 - 131.
Department(s) Operations Research and Logistics
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Scheduling; Offshore Wind Farm; Heuristic; Fleet composition; Maintenance planning
Abstract 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.
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