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dc.contributor.authorGutierrez-Alcoba, Alejandro
dc.contributor.authorHendrix, Eligius M.T.
dc.contributor.authorLopez, Gloria Ortega
dc.contributor.authorHalvorsen-Weare, Elin Espeland
dc.contributor.authorHaugland, Dag
dc.date.accessioned2020-05-14T13:32:26Z
dc.date.available2020-05-14T13:32:26Z
dc.date.issued2019
dc.PublishedGutierrez-Alcoba A, Hendrix EM, Lopez, Halvorsen-Weare EEH, Haugland D. On offshore wind farm maintenance scheduling for decision support on vessel fleet composition. European Journal of Operational Research. 2019;279(1):124-131eng
dc.identifier.issn1872-6860en_US
dc.identifier.issn0377-2217en_US
dc.identifier.urihttps://hdl.handle.net/1956/22274
dc.descriptionUnder embargo until: 2021-04-25
dc.description.abstractMaintenance 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.en_US
dc.language.isoengeng
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivs CC BY-NC-NDeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/eng
dc.titleOn offshore wind farm maintenance scheduling for decision support on vessel fleet compositionen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2020-01-17T13:46:16Z
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2019 Elsevieren_US
dc.identifier.doihttps://doi.org/10.1016/j.ejor.2019.04.020
dc.identifier.cristin1705871
dc.source.journalEuropean Journal of Operational Research


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