In recent years, several logistic optimisation models have emerged as powerful tools to assess and optimise the planning, costs, and workability of marine operations. Nevertheless, these models often rely on two underlying assumptions: (1) the significant wave height and peak wav
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In recent years, several logistic optimisation models have emerged as powerful tools to assess and optimise the planning, costs, and workability of marine operations. Nevertheless, these models often rely on two underlying assumptions: (1) the significant wave height and peak wave period adequately describe (directional) wave fields, and (2) these two parameters sufficiently describe the conditions causing weather downtime. However, little is known about how these underlying assumptions affect the reliability of logistic optimisation tools. This study, therefore, presents an alternative model that integrates response motions of vessels and turbine structures into a logistic optimisation tool to address and expose the implications that follow from using these assumptions. A case-study approach, on two recently realised offshore wind farm projects, showed that integrating response motions results in, up to 10%, less favourable workability conditions. Further analysis on the data showed that it is crucial to include the two-dimensional wave energy distribution to expose more complex sea states that induce weather downtime. Moreover, a failure analysis approach found that the conditions inducing downtime events are more accurately described by response motions instead of sea state parameters. Therefore, the findings of this study suggest that integrating response motions into logistic optimisation models improves the reliability of the model estimates. Besides, this study suggest that the industry’s approach potentially overestimates the true workability and, therefore, imposes unnecessary operational risks. Hence, the results of this study demonstrate the importance of integrating hydrodynamic engineering knowledge into the assessment and optimisation of project planning, costs, and workability.