The logistics and supply chain management of OWF maintenance have a fundamental impact on their availability and, as a result, profitability. The issues of servicing WTs and OWF maintenance, in general, are currently very often discussed in the literature on the subject [13,14,15,16]. Decision-making, including the management of spare-part inventory, the purchase or lease of consumables, the outsourcing of repair services, the organization and planning of maintenance tasks, and the determination of vessel routes, as well as the selection of OWF maintenance strategies are the most frequently addressed areas of research. The main goal of maintenance strategies is to reduce O&M costs and to improve WT reliability. The decisive share in these costs falls on the handling and maneuvering of service operation vessels; the selection, import, and collection of appropriate components (forming repair kits) intended for the repair of planned WTs; and the completion of tasks by service teams and UAV operators.

Much work has been devoted to the problems of routing and scheduling service vessels. The essence of these problems comes down to optimally assigning WTs and routes to the vessels, as well as minimizing costs related to traveling to the respective WTs [1,12,17]. Relatively few studies deal with uncertainties such as weather-related vessel movement to determine operability. The limitations due to changes in weather conditions (particularly, substantial wave heights and wind speeds) strongly determine the accessibility of WTs to service vessels and personnel transfers from the vessel to the WT.

Traditionally, the visual inspection of WTs by experienced technicians is, in addition to being dangerous, very laborious and time-consuming. It is also very resource-intensive. This work can be successfully performed by UAVs equipped with high-resolution cameras that can detect various types of damage, such as fatigue cracks, surface corrosion, galvanic corrosion, pitting, stress corrosion cracking, and erosion.

In addition to monitoring and carrying out minor maintenance repairs, UAVs are increasingly used to transport spare parts and tools from the vessel to the WT requiring repair [3]. Such a UAV-based solution for the delivery of not very heavy loads allows for a reduction in the limitations related to the availability (e.g., in the case of mooring) of vessels and the dependence on meteorological and environmental conditions. The main limitation of this type of application is an electric drive limiting a UAV’s operating time. Examples of research conducted in the field of UAV energy supply management are presented in [4,18,19].

The considered problem of planning service missions involving the routing/scheduling of a vessel transporting service teams and UAVs delivering spare parts to serviced WTs is a special case of ground–vehicle and unmanned aerial vehicle routing problems (GV-UAV) [20], assuming that the UAVs’ base is an object moving on land. In the literature, there are numerous contributions dealing with this subject [21,22,23,24,25]. However, they do not address the issues of planning missions in a maritime environment, where weather conditions are of great importance to these missions. From this perspective, the proposed declarative model for planning WT service missions fills the gap in the research on the use of UAVs in maritime environments.

The models and methods used in OWF maintenance vary depending on the tasks undertaken each time. The algorithms implemented in these build upon previous experience gained in solving problems arising in a variety of UAV applications, ranging from precision farming [26] to disaster management [27] and infrastructure inspection [28], as well as in various other fields, including the defense, civilian, and commercial sectors. They include stochastic models related to, for example, forecasting service windows, periodic inspections, and organization of supply chains; operational research models (based on mixed-integer programming, dynamic programming, etc.); simulation models (used to determine the trajectory of UAV flight ship routes, etc.); and artificial intelligence models and fuzzy models (using, e.g., population algorithms such as ant colony [29], beetle swarm [30], and system-improved grey wolf optimization [31], as well as fuzzy logic algorithms such as fuzzy reinforcement learning [32], fuzzy particle swarm optimization [33,34], and fuzzy C-means [35]). Formal representations of these models implemented in the relevant methods of imperative programming allow for formulating and solving problems related to the so-called analysis of a problem situation, i.e., related to the search for an answer to the question of whether (what) set values of a set of decision variables guarantee a specific (extreme) value of the assumed objective function. This means that searching for answers related to the so-called synthesis of a problem situation, i.e., related to the search for an answer to the question of whether there are (and if so, what) such values of set decision variables at which the adopted objective function reaches a specific (extreme) value, is not allowed.

Moreover, none of the detailed models presented earlier meet the requirements for use both in the construction of an integrated model of OWF maintenance and in the formulation of synthesis-type problems. Models implementing the declarative programming paradigm have the greatest chance of meeting these expectations. The constraint-programming strategies used in these models enable the formulation of both analysis and synthesis problems due to these models inherently having open structures.

Unfortunately, the declarative approach is very rarely used both for modeling and solving OWF logistics problems. This deficiency is visible, among others, in the need for studies covering reactive OWF maintenance planning, related to the generation of scenarios that would suspend or stop an initiated inspection and/or repair missions. The presented research gap is address by relatively few studies [17,36,37,38]. It is easy to see that filling this gap will contribute to the creation of systems supporting the dispatcher in planning missions related to the maintenance of OWFs.