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dc.contributor.authorFan, Dongming
dc.contributor.authorRen, Yi
dc.contributor.authorFeng, Qiang
dc.contributor.authorZhu, Bingyu
dc.contributor.authorLiu, Yiliu
dc.contributor.authorWang, Zili
dc.identifier.citationJournal of Loss Prevention in the Process Industries. 2019, 62 1-13.en_US
dc.description.abstractAs the operation and maintenance (O&M) costs constitute a substantial portion of the overall life-cycle cost of offshore wind farms, routing, and scheduling of maintenance are very important for cost reduction. With the multi-type of vessels, multi-period, multi-base of O&M, multi-wind farm and uncertain weather conditions, the optimization of O&M cost is more challenging. In this article, a hybrid heuristic optimization of maintenance routing and scheduling for offshore wind farms is proposed. First, with the maintenance service protocol, mixed particle swarm optimization (MPSO) is applied to seek a desired mapping relation between vessels and wind farms. Utilizing the formalized rules, an optimal vessel allocation scheme is explored in the large solution space by individual crossover, swarm crossover and mutation. Then, with the scheme of vessel allocation, a discrete wolf pack search (DWPS) is introduced to optimize the maintenance route under all constraints. As the evaluation standard of MPSO, the purpose of DWPS is to search the solution space with depth and breadth balanced and find the optimal and open maintenance route with multiple round trips to bases that minimize O&M costs, including travel, technician and penalty costs. Finally, computational experiments and analysis are carried out. The results provide both the optimized cost and detailed arrangements, which can be directly used in the maintenance schedule.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleA hybrid heuristic optimization of maintenance routing and scheduling for offshore wind farmsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalJournal of Loss Prevention in the Process Industriesen_US
dc.description.localcode© 2019. This is the authors’ accepted and refereed manuscript to the article. Locked until 5 September 2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
cristin.unitnameInstitutt for maskinteknikk og produksjon

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal