Recovery-informed collaborative scheduling of offshore maintenance resources under extreme scenarios

Offshore wind farms are vulnerable to extreme weather and concurrent failures, which simultaneously increase maintenance demand, restrict vessel accessibility, and reduce recoverability of the system after maintenance. The coordination of maintenance resources becomes critical for sustaining offshore operation continuity under extreme conditions. To address this issue, this paper proposes a recovery-informed multi-objective optimization framework for collaborative offshore maintenance resource scheduling. The proposed framework combines risk-aware topological modeling, heterogeneous resource collaborative scheduling, recoverability evaluation and operation continuity evaluation, and is optimized by a two-layer genetic algorithm. A case study of an offshore wind farm is conducted under daily operation and maintenance (O&M), extreme high-load, and emergency maintenance scenarios. The results show that the proposed model can generate feasible scheduling schemes and improve system recoverability and post-disruption operation continuity. Further sensitivity analyses verify the effects of wind speed, interruption tolerance time, and vessel backup on maintenance scheduling decisions. The findings highlight the value of collaborative scheduling for offshore maintenance decisions under extreme scenarios.