Offshore windmill and substation maintenance planning with Distance, Fuel consumption and Tardiness optimisation
De Kuyffer, E.; Shen, K.; Martens, L.; Joseph, W.; De Pessemier, T. (2023). Offshore windmill and substation maintenance planning with Distance, Fuel consumption and Tardiness optimisation. Operations Research Perspectives 10: 100267. https://dx.doi.org/10.1016/j.orp.2023.100267
In: Operations Research Perspectives. Elsevier: Amsterdam. e-ISSN 2214-7160, meer
Despite a lot of research about predictive maintenance for onshore and offshore windmill farms, nearly no investigation has been performed to obtain the optimal sequence in which windmills are to be served in a predefined time frame. The higher fuel costs and the increasing time pressure on maintenance jobs urge the need for optimisation, so offshore windmills can be serviced at minimal costs and within a limited time frame. To minimise distance travelled, fuel consumption and average tardiness of all maintenance tasks to be carried out, a multi-objective, non-dominated sorting island model of genetic algorithms is used. The following novel contributions are realised: (i) A multi-objective island model is used, where on each island a different genetic algorithm is used to minimise a separate cost function per island. (ii) A set of non -dominated maintenance sequences, shown as a Pareto plane, are computed and (iii) these optimal solutions can be used by the planner to select the route to be followed by the CTV when travelling from windmill to windmill during a maintenance sequence. Tests on two of the islands have resulted in a relative improvement of around 65 to 70% on fuel consumption and distance in relation to a random sequence, while the third island has generated a relative gain of 69% in average weighed tardiness. The three islands combined have resulted in a set of Pareto optimal sequences for offshore windmill maintenance.
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