## 3 Results
### Evaluation of the Influence of Limited Resources Without Forecast Uncertainties  
This experiment aims to provide insights into the relationship between the number of vessels and loading bays under different weather conditions. For the latter, the evaluation focusses on June, \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**2000** & **Apr.** & **May** & **Jun.** & **Jul.** & **Aug.** & **Sep.** & **Oct.** & **Mean** \\ \hline Act. dur. Min & 19 & 19 & 19 & 19 & 19 & 19 & 19 & 19.00 \\ Act. dur. Mean & 23 & 23 & 28 & 30 & 20 & 26 & 31 & 25.86 \\ Act. dur. Max & 69 & 81 & 85 & 101 & 41 & 74 & 130 & 83.00 \\ \hline \multicolumn{10}{l}{Standard Deviation: Sliding Window in hours} \\ \hline
1 Week & 0.22 & 1.77 & 1.41 & 2.21 & 0.00 & 0.57 & 0.86 & 1.01 \\
2 Weeks & 2.16 & 1.25 & 3.29 & 3.67 & 0.00 & 3.11 & 2.10 & 2.23 \\
3 Weeks & 2.10 & 3.07 & 20.81 & 20.15 & 0.55 & 10.18 & 15.93 & 10.40 \\
4 Weeks & 35.82 & 77.29 & 31.79 & 17.45 & 1.79 & 94.42 & 335.87 & 84.92 \\ \hline \multicolumn{10}{l}{Standard Deviation: Markov in hours} \\ \hline
1 Week & 0.26 & 0.67 & 2.56 & 0.58 & 0.00 & 1.47 & 0.77 & 0.90 \\
2 Weeks & 1.07 & 0.53 & 1.99 & 2.28 & 0.08 & 2.86 & 6.30 & 2.16 \\
3 Weeks & 1.14 & 0.95 & 4.06 & 12.73 & 0.29 & 5.02 & 5.59 & 4.25 \\
4 Weeks & 1.08 & 3.07 & 6.78 & 11.02 & 1.26 & 5.26 & 77.85 & 15.19 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the sliding window and the Markov approach. Figure 6: Estimation for the Markov-Chain and Sliding-Window approaches compared to the real duration. August, and October 2000. As given in Table 4, August provides close to perfect weather conditions, while October has the worst still suitable weather conditions in 2000. June was selected as it provides a comparably moderate mix of good and bad conditions. For the assessment of the impact of loading bay restrictions, this evaluation uses different combinations of vessels and bays over a time frame of three weeks. Therefore, it covers different ratios between vessels and bays. The evaluation also includes some experiments with equal ratios to ensure that this ratio provides a suitable indicator. The parameter selection for \(V\) and \(BAYS\) thereby followed a factorial design with the aim, to achieve an as broad as possible coverage of ratios between these two. All the optimizations use the MILP formulation in Section 2.4, without the inclusion of weather uncertainties. Table 5 summarizes the results. Figure 7 depicts the number of finished turbines per vessel (a) and the normalized bay use (b) for different ratios between bays and vessels for each of the evaluated months. The figure shows that the general shape for each month is very similar. Therefore, a high ratio results in a high number of finished turbines and a low bay use. In contrast, for ratios of less than 0.33, the use reaches a plateau. Deploying more than three vessels for a single bay does not increase the number of finished turbines in any of the evaluated months. Furthermore, the figure shows a strong influence of the weather conditions on the overall number of finished turbines and the bay use. While the deployment of only a single vessel per bay results in a comparably equal number of finished turbines per vessel, it declines faster for worse conditions. The results show that the number of finished turbines per vessel decreases by 7.5% for a ratio of 0.75 if comparing the average number of finished turbines to the best case (ratio of 1). This loss increases to 10% for a ratio of 0.5 (two vessels, one bay) and drastically increases to 21.67% for a ratio of 0.33 (three vessels one bay). As stated before, after this point, a steady, linear decrease can be observed, as the number of finished turbines stagnates. In conclusion, these results show that the efficiency of deployed vessels and the use of bays strongly depends on available weather windows. The results indicate that three vessels per bay constitute the maximum sensible amount if aiming for high use, at the cost of an average of 21.67% efficiency.