Advanced optimisation software framework for floating offshore wind farm logistics and operations

Offshore wind farms are being built farther from shore to maximise energy production, but this creates significant logistical challenges for maintaining these remote turbines. Service Operation Vessels (SOVs) and Crew Transfer Vessels (CTVs) play a vital role in keeping turbines operational. However, their routes need to be carefully planned to reduce travel distances and fuel consumption while meeting tight maintenance schedules. This study introduces a Python-based optimisation framework designed to streamline both day-to-day and campaign-style maintenance operations. By integrating geospatial analysis, clustering algorithms, and multi-day scheduling, the framework generates efficient vessel routes considering different turbine locations, weather windows, technician capacity, and vessel availability. When full daily servicing is unfeasible, the framework prioritises tasks and creates multi-day schedules to ensure efficient use of resources. Clustering techniques further streamline the process by grouping nearby turbines for maintenance. A real-world case study demonstrated a 36 % reduction in fuel consumption compared to conventional methods, underscoring the framework's potential to lower operational costs and enhance sustainability. In addition to this efficiency gains, the solution mitigates risks by ensuring timely maintenance, thereby supporting the offshore wind sector's capacity to meet escalating energy demands reliably. • Proposes an optimisation framework for efficient planning of offshore turbine maintenance. • Improves Service Operation Vessels (SOVs) and Crew Transfer Vessels (CTVs), routing to cut travel distance and fuel use. • Uses Python-based heuristic optimisation and clustering for flexible maintenance scheduling. • Validated with real-world data from offshore wind operations and maintenance company, proving practical effectiveness. • Provides multiple scheduling options, allowing operators to consider various constraints.