The offshore wind industry is rapidly growing, and so is the competition in this market. Normally, offshore installation companies receive a subsidy for building offshore wind farms, but due to multiple companies bidding to install for lower and lower subsidies, the point where wind farms are installed without subsidy has been reached. Therefore, companies have to install wind farms as efficient as possible. Since logistics are a major contributor to costs during the installation of an offshore wind farm, Van Oord and Deltares are doing research methods to solve logistic and scheduling problems. In this thesis, research is done in algorithms to solve two typical planning problems which arise during the installation of offshore wind farms: The Component Relocation Problem (CRP) and the Ship Installation Routing Problem (SIRP).


In the CRP, the storage and movement of turbine components in the harbour is considered. The crane operator manages the storage yard to fulfil a schedule of arriving and departing components. Since access of a storage location by crane can be hindered due to surrounding components, placement and relocation of components such that the requested component is accessible at the requested time, is a challenge. This challenge is solved in the component relocation problem, while minimizing the amount of relocations carried out by the crane.

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A Mixed Integer Linear Programming (MILP) model is developed for this problem, along with an improved formulation to increase the solver performance and solve small instances to optimality. For practical use during offshore installation projects, a rolling horizon heuristic algorithm is developed.

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In the SIRP, an installation schedule with corresponding ship routes is created. To install a turbine, multiple components have to be transported to the required locations, where they will be installed by multiple ships. While creating an installation schedule, it is desired to both minim the travelling time for ships, as the time ships are waiting on other ships and objects. An MILP for this scheduling problem is given, which models this problem as a Vehicle Routing Problem, with extensions to account for installation by multiple vehicles and time synchronization. Furthermore, to solve larger problems, an Adaptive Iterative Simulated Annealing (AISA) algorithm is developed.


The heuristic methods were validated against the exact methods and for small instances the heuristic methods were able to replicate the optimal solutions. The heuristic algorithms provided decisions in less than 12 hours, a relatively short time in comparison to installation times for offshore wind turbines. The AISA was able to create a schedule which drastically decreased the synchronization conflicts in a real world installation project. Although the sailing distance increased in comparison with the original planning, it was improved compared to the executed route. Furthermore, for the same project, the crane routing optimisation led to a reduction of 55% based on non-essential crane actions.