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The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there exist several models for scheduling offshore operations, which focus on vessels but neglect the influence of resource restrictions at the base port and uncertainties involved with weather predictions. This article proposes a Mixed-Integer Linear Programming model for the scheduling of installation activities, which handles several installation vessels as well as restrictions about available cargo bridges at the port. Additionally, the article explains how this model can be combined with a Model Predictive Control scheme to provide decision support for the scheduling of offshore installation operations. The article presents numerical studies of the effects induced by resource restrictions and of different parametrizations for this approach. Results show that even small planning windows, paired with comparably low computational times, achieve reasonably good results. Moreover, the results show that an increase in vessels comes at diminishing returns concerning the installation efficiency. Therefore, the results indicate that available good-weather windows primarily limit efficiency.

Purpose: This article aims to identify common structural elements in the descriptions of both approaches, enabling the application of model transformations. Methodology: Several models of both types will be compared, combining relevant concepts, i.e., entities, attributes and relationships into a generalized model. In a second step, elements crucial to either type of model are identified. For the remaining elements, interdependencies and redundancies will be identified to enable a model reduction. Findings: While the structure and notation of both approaches are different, both describe the same fundamental concepts and relationships. The article provides a data model of these common concepts for the operational planning of offshore activities, including weather restrictions and forecasts. Originality: In current literature, there exist no approaches to combine mathematical optimization with event-discrete simulations in the context of offshore wind farm installations. To harness the advantages of both approaches in an integrated methodology, a model of common concepts is required, which does not exist at this time.