## 1 Introduction  
Wind energy constitutes one of the most promising technologies to cover the world's need for sustainable energy. In 2017, wind farms with a capacity of around 52 Gigawatts were constructed worldwide, increasing the total amount of energy produced by wind turbines by approximately \(11\%\) to a total of 539 Gigawatts [1]. As a result of high wind speeds at sea, offshore wind farms can produce large amounts of energy [2, 3]. According to the Renewables 2018 Status Report [1], the amount of energy generated by offshore wind farms was exponentially increasing over the last decade. Whileoffshore wind farms provide a steadier stream of energy at higher rates compared to onshore wind farms, their construction poses particular challenges due to their increased weight and dimensions. Also, the installation process requires highly specialized, expensive vessels, and resources [4; 5]. In general, logistics costs during the installation can amount to about 15% to 20% of the costs for offshore wind farms [6; 7; 8]. As a result, the efficient use of available resources constitutes a predominant factor of success. On the one hand, this requires optimal planning and scheduling of available resources; on the other hand, highly dynamic weather conditions at sea render the execution of such plans difficult. Consequently, adaptive, model-based decision-support systems can be used to achieve optimal planning while retaining a high degree of adaptability to these dynamic conditions [9]. Most of the models found in the literature focus on the simulation or scheduling of offshore operations, using a provided set of vessels. These models are often used to evaluate different amounts of vessels, e.g., [10; 11]. Nevertheless, efficient use of vessels also requires considering the availability of resources at the base port. For example, loading components for an offshore wind turbine at port takes several hours per part, approximately amounting to a total of 12 h per turbine. If only a single loading bay is available, this long duration can quickly render port operations to become a bottleneck if several vessels are applied. As companies usually charter vessels and bays for extended periods, optimization models should be capable of handling such restrictions. Unfortunately, current models do not include restrictions on loading bays, which could render their results inaccurate and lead to an underestimation of the project duration. In turn, such underestimations may result in delays and, thus, in high additional costs and the need for rescheduling. Moreover, current models generally try to optimize the overall project with a more or less unlimited prediction horizon. Such long horizons require exact and reliable long-term weather forecasts, which are not available in reality. This article extends the Mixed-Integer Linear Programming (MILP) optimization model and the overall Model Predictive Control (MPC)-based approach presented in [9] to handle several vessels and different numbers of loading bays to achieve more realistic plans under forecast uncertainties. Additionally, it presents an enhanced method to preprocess weather forecast uncertainties for their use during the optimization. The MILP model still aims to find optimal schedules for previously decided numbers of vessels and bays but can provide a foundation to optimize these numbers in future work. In this context, the article also describes significant extensions to the MPC-based methodology to enable usage of the extended optimization model as decision support under weather forecast uncertainties by deriving plans incrementally. Finally, this article provides numeric studies of different configurations with regards to vessels and bays, to assess the impact of loading restrictions on the performance of the installation process as well as to evaluate the extended approach in general. The next Section 1.1 presents a description of the installation process and its characteristics.