# Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance
## 6 Conclusions  
Addressing the issue of planning maintenance logistics for offshore wind farms, this paper proposes a constraint-programming-driven reference model for proactive scheduling and routing of vessels that deliver/receive service teams to/from selected WTs and for reactive mission planning of a fleet of UAVs delivering repair kits to WTs via ad hoc ordering. With these assumptions, the goal of the research was to develop a reference model enabling the implementation of methods of computer-aided online service mission planning for problems of a scale encountered in practice (i.e., 6-8 WTs serviced during the day). The solutions found thus far are mostly limited to land conditions and do not take into account the impact of the environment, in particular the sea state and weather, on the feasibility of missions. The declarative nature of the model allows for its implementation in commercially available constraint programming environments, e.g., ILOG, ECLiPSe, and Gurobi. Figure 9: Calculation time for a different number of service teams and a different number of WTs. The proposed approach's main advantage is the model's open structure, which allows for the consideration of new relations between decision variables. This is carried out without loss of computational efficiency because, in the used constraint programming environment, the time it takes to solve a problem decreases as the number of constraints increases. The presented case studies implementing this model illustrate its usefulness in searching for solutions of both analysis and synthesis problems, which come down to checking whether the given resources and the way in which they are used, respectively, guarantee maintenance of the OWF, or what resources are required and how they are to be used. This research was based on fragmentary or approximate data. Their confidentiality, as well as the lack of access to relevant statistics, made it impossible to verify the usefulness of the proposed approach. The collected results from the computer experiments, however, confirm its usefulness in computer-aided logistics planning systems for missions related to the maintenance of OWFs. The results of the conducted experiments confirm the possibility of using the developed approach in situations where the number of serviced WTs \(N1\leq 8\), and number of service groups \(W\leq~{}5\). The limitations of the proposed approach result from the characteristics of marine coastal environments (concerning both current sea states and weather forecasts, and related seasonal statistics), the lack of standards in the field of cost accounting (taking into account the shaping, ordering, and storage of spare-part kits; the servicing of WTs, which requires their shutdown; and the employment of workers with different qualifications, implying different productivities and labor costs), the nature of the disruptions (e.g., device failures, employee absenteeism, and changes in organizational structure), etc. To overcome these limitations, future research should focus on extending the model to taken into account the ability to proactively plan a fleet of vessels and to reactively plan joint UAV missions for vehicles stationed at different vessels. Future work should also extend the model to cases related to the uncertainty of operation times, including service team operation, vessel sailing, and UAV flights (expressed in fuzzy numbers), while meeting expectations related to minimizing the total cost of maintaining a serviced OWF. Conceptualization: G.R., Z.B. and G.B. ; methodology: G.R., Z.B. and G.B. ; software: G.R. and B.G; validation: [PERSON], R.F. and G.B. ; formal analysis: G.R., G.B. and R.F. ; investigation: Z.B. and I.N. ; resources: I.N. and R.F. ; data curation: I.N. and R.F. ; writing--original draft preparation: [PERSON], Z.B. and G.B. ; writing--review and editing: Z.B., R.F. and I.N. ; visualization: G.R. and G.B. ; supervision: Z.B. and I.N. ; project administration: Z.B. and I.N. ; funding acquisition: I.N. and R.F.