Offshore Wind Farm Maintenance Scheduling Optimization

Offshore turbine maintenance faces a costly dilemma — service too early and production is wasted, too late and turbines sit idle — while conventional schedulers ignore the hidden cost of turbine production loss. The team built a globally optimal scheduling model that jointly accounts for production loss, overtime labor, and vessel travel cost, solved by a purpose-built genetic algorithm. Validated on real offshore wind farm data, the system outputs complete maintenance Gantt charts, vessel routes, and technician assignments, and has been productized with a leading wind-power enterprise.

1. 1

   Make the Invisible Loss Visible

   The gap between healthy and degraded power curves, combined with 3-day wind-speed forecasts at 15-minute resolution, quantifies each turbine’s production loss before and during maintenance. This hidden loss can account for roughly 40% of the overall maintenance cost, yet conventional schedulers routinely ignore it.

2. 2

   A Three-Layer Architecture from Data to Decisions

   The bottom layer ingests per-turbine SCADA, condition-monitoring, and ambient data; the middle layer combines a PHM module that generates maintenance tasks with weather-forecast and SCM/ERP/MES modules that supply weather windows and resource constraints; the top layer’s optimizer outputs schedules, vessel routes, technician assignments, and cost breakdowns in one pass.

3. 3

   Mixed-Integer Formulation with a Custom Genetic Algorithm

   Maintenance sequence and vessel waiting time are encoded as two families of decision variables in a purpose-built genetic algorithm (GA) solver, replacing hours-long commercial-solver runs and delivering near-optimal executable plans within minutes even at large scale.

4. 4

   From Shortest Route to Lowest Total Cost

   The solver prioritizes turbines with high production loss even when they are not closest to the harbor. On real data from a farm with 27 turbines of 4 MW, the schedule cut total cost markedly versus a production-loss-blind baseline, with overtime cost driven to zero.