## 5 Experimental Results
### Performance of MILP and CP Models
#### 5.1.2 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Instance Characteristics**} & \multicolumn{3}{c}{\(MILP_{\mathrm{wav}}\)} & \multicolumn{2}{c}{\(CP_{\mathrm{wav}}\)} \\ \hline
**Instance** & **N** & **O** & \(T_{MILP}\) & \(CP\)\(U_{MILP}\) & \(T_{CP}\) & \(CP\)\(U_{CP}\) \\ \hline LWOS1 & 30 & 66 & **0** & 320 & **0** & 6 \\ LWOS2 & 30 & 80 & **5760** & 850 & **5760** & 6 \\ LWOS3 & 40 & 88 & 9852 & 1710 & **9712** & 6 \\ LWOS4 & 40 & 96 & – & \textgreater{}1800 & **9980** & 6 \\ LWOS5 & 50 & 110 & – & \textgreater{}1800 & **12**,100 & 12 \\ LWOS6 & 50 & 127 & – & \textgreater{}1800 & **19**,560 & 12 \\ LWOS7 & 65 & 143 & – & \textgreater{}1800 & **19**,800 & 12 \\ LWOS8 & 65 & 150 & – & \textgreater{}1800 & **21**,600 & 30 \\ LWOS9 & 65 & 165 & – & \textgreater{}1800 & **22**,400 & 26 \\ LWOS10 & 65 & 185 & – & \textgreater{}1800 & **23**,980 & 26 \\ LWOS11 & 70 & 164 & – & \textgreater{}1800 & **23**,800 & 30 \\ LWOS12 & 70 & 172 & – & \textgreater{}1800 & **25**,000 & 42 \\ LWOS13 & 70 & 190 & – & \textgreater{}1800 & **26**,960 & 42 \\ LWOS14 & 75 & 182 & – & \textgreater{}1800 & **26**,740 & 73 \\ LWOS15 & 75 & 198 & – & \textgreater{}1800 & **27**,880 & 73 \\ LWOS16 & 75 & 212 & – & \textgreater{}1800 & **29**,**660 & 73 \\ LWOS17 & 80 & 210 & – & \textgreater{}1800 & **29**,**920 & 49 \\ LWOS18 & 80 & 225 & – & \textgreater{}1800 & **38**,500 & 70 \\ LWOS19 & 100 & 320 & – & \textgreater{}1800 & **40**,**940 & 87 \\ LWOS20 & 100 & 380 & – & \textgreater{}1800 & **48**,520 & 87 \\ Average & 65 & 173 & – & – & **23**,**141** & 38 \\ \hline \hline \end{tabular} Optimal values in bold.  
\end{table} 
Table 6: Main characteristics of the considered large-sized instances and comparison of MILP\({}_{\mathrm{wav}}\) and \(CP_{\mathrm{wav}}\) models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Instance Characteristics**} & \multicolumn{3}{c}{\(MILP_{\mathrm{RC}}\)} & \multicolumn{2}{c}{\(CP_{\mathrm{RC}}\)} \\ \hline
**Instance** & **N** & **O** & **U** & \(T_{MILP}\) & \(CP\)\(U_{MILP}\) & \(T_{CP}\) & \(CP\)\(U_{CP}\) \\ \hline RCWOS1 & 5 & 7 & 1 & **0** & 10 & **0** & 302 \\ RCWOS2 & 5 & 12 & 1 & **0** & 14 & **0** & 302 \\ RCWOS3 & 5 & 20 & 2 & **0** & 18 & **0** & 302 \\ RCWOS4 & 8 & 10 & 2 & **0** & 15 & **0** & 302 \\ RCWOS5 & 8 & 25 & 2 & **0** & 58 & **0** & 302 \\ RCWOS6 & 8 & 30 & 3 & **0** & 82 & **0** & 950 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Main characteristics of the considered small-sized instances and comparison of \(MILP_{\mathrm{RC}}\) and \(CP_{\mathrm{RC}}\) models. If we analyze the results when solving the MILP\({}_{\text{RC}}\) and CP\({}_{\text{RC}}\) models with small-sized instances that consider resource calendar constraints, the presence of a high number of unavailability periods decreases, even more regarding the performance of the MILP model (the model only obtains 15 out of 20 optimal solutions and 20 of 20 feasible solutions). The CP model, on the other hand, seems to perform well and is still able to obtain optimal solutions even when the number of unavailabilities is high. 2. Medium- and Large-Sized Instances  
The computational results for the medium/large-sized problems are summarized in the Table 8 below.