This work presents a prototype of a decision-support system for Offshore Wind Farm (OWF) construction based on the Timed Petri-Nets (TPN) approach, which aims to help the decision-maker in the industry to optimize the process execution. The objective function evaluates the solutions or alternatives to find the optimums. We also present a decoupled scheduling strategy that is computationally efficient and stable comparing to the Mixed-Integer Linear Programming method, which is mathematically exact. Nonetheless, the decision-support system also supports the case, in which multiple agents or installation vessels are used in the projects. The numerical study reveals the relation between the process acceleration and charter cost by using two installation vessels. Last, historical data are used for the weather predictions.


Deploying wind energy is one of the ways of getting climate change tractable. Studies show that 2020 was the warmest year in history in Europe and the last six years were the warmest six on record (C3S 2021). The European Union (EU) aims to be climate-neutral by 2050 – an economy with net-zero greenhouse gas emissions (European Commission 2020). Following the EU, China endeavors to achieve carbon neutrality by 2060 (Mallapaty 2020). Even though wind energy is the fifth-largest energy source worldwide, its share of the world gross electricity production counts only 4.3%, which is less than half of the electricity production of nuclear power, the fourth-largest energy source worldwide, with a share of 10.1% (IEA 2020). Fig. 1 gives an overview of the share of energy consumption worldwide in 2019.

One of the major weaknesses of wind energy is the high installation cost, which mostly results from severe offshore weather conditions. A possible way to mitigate this enormous expenditure is through effective scheduling and rescheduling. However, it is not an easy task to schedule the offshore installation process, since it is heavily influenced by the offshore weather condition, which is changeable and partially predictable. Besides, other sorts of unstable factors, e.g., the crew's illness, are hard to quantify. Thus, we present a prototype of a decision-support system to help the operator make decisions during the installation process, which can schedule and reschedule the installation tasks and consider the nonsystematic influences as information from the real physical system.

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