## 1 Introduction
### Planning Approaches for the Installation of Offshore Wind Farms  
In current literature, only a few works deal explicitly with the installation planning of offshore wind farms [5; 12]. In general, these approaches can be classified in either simulation-based approaches, used to evaluate given configurations, or in optimization-based approaches, aiming to generate optimized plans for certain aspects of the overall planning problem.  
In terms of simulation-based models, all current approaches focus on the evaluation of the involved actors' behavior, usually concentrating on vessels, in terms of the overall installation performance. [PERSON] et al. present an approach to compare the effect of deterministic and stochastic assumptions concerning weather conditions [8]. [PERSON] et al. propose a simulation model to evaluate different pre-assembly strategies and to assess their impact on the overall installation process [12]. [PERSON] et al. propose a multi-agent-based model to compare the conventional installation concept with feeder concepts, later extending the simulation model for different feeder concepts [14; 15]. While all these approaches simulate the behavior of vessels and thus can be used to derive a plan after the simulation finished, they do not include an optimization component to evaluate different schedules or configurations. Whereas most of these models include storage capacity limitations, it is not explicitly stated or investigated, if the used simulation models support constraining operative resources at the base port.  
In terms of mathematical models, the majority of literature focuses on the generation of plans on different levels of abstraction. Therefore, most authors use Mixed-Integer Linear Programming formulations to tackle their optimization problem. [PERSON] et al. apply a heuristic approach to plan the deployment and decommissioning of vessels, thereby optimizing the overall cost of the installation process [20]. [PERSON] et al. combine a precedence-based job-shop scheduling formulation with a multi-periodic production formulation to derive optimal plans for small scenarios with a daily resolution [21]. Later, they extend this model by heuristics to enable solving larger problem instances [22]. The same model was recently extended by [PERSON] et al. to deal with probabilistic weather assumptions [23]. Besides their multi-agent-based approach, [PERSON] et al. propose another Mixed-Integer model, which closely resembles a time-indexed job-shop formulation [10]. For each indexed period of 12 hours, the optimizer determines the number of foundations, cables, and top structures to be built using a predefined vessel configuration. [PERSON] et al. propose a bi-objective, Mixed-Integer optimization model, aiming to find a trade-off between minimal construction times and minimal costs [11]. The authors recently extended this model for the decommissioning of offshore wind farms in [24].  
As can be seen from these descriptions, most optimization models focus on the handling of vessels, operations, and weather conditions. Only a few models include port-related features [25], again, only focusing on port storage capacities. The mentioned models do not cover the availability of supporting resources such as loading bays and stationary cargo bridges. Moreover, the majority of the presented models try to achieve schedules for the overall installation project and thus assume that weather conditions are known a-priori. As described in the process description and the state of the art, historical information can be used to achieve reasonable estimations, but are not suitable to support the operative decision-making process, as they are not designed to handle dynamic weather conditions at sea. In terms of decision-support systems, current approaches are monolithic per design, i.e., they derive the complete plan once, and do not perform any iterations or increments during the execution. Thus, there exist no monitoring or intervention policies.