## 4 Experimental Results
Therefore, our study aims to show whether it is possible to improve the APD-generated solutions by applying DCS. However, comparisons within the same group of models can be made because they share the same parameters and have the same number of solutions. To allow fair comparisons within the models of ADP-DCS, all models in this family are terminated after DCS has generated 500 individuals. The computational results are shown in Table 1 for j30, j60, and j90. Table 3 shows the average values of the NPV for the j30, j60, and j90 project sets corresponding to the six models for different numbers of event sets. The results show that the mean values of ADP-DCS are better than ADP for all three project sets. We also see that the mean values produced by EDA and ACO as priorities for MC are better than the mean values of the random priority for MC. In particular, EDA priority has generated the highest average values in the majority of the cases. Figures 2 and 3 show the bar charts of the mean values of the NPV plotted against different job sets by varying the size of event occurrences. We see that ADP(-DCS)-EDA consistently outperforms the other methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Priority Rule** & \(|\mathbf{E}|\) & \(\overline{\mathbf{Z}}\)* for j30 & \(\overline{\mathbf{Z}}\)* for j60 & \(\overline{\mathbf{Z}}\)* for j90 & **Overall \(\overline{\mathbf{Z}}\)* \\ \hline \multirow{8}{*}{ADP} & \multirow{4}{*}{Random} & 3 & 25.18 & 132.47 & 212.65 & 123.43 \\ \cline{3-6}  & & 4 & \(-\)17.05 & 64.72 & 126.22 & 57.96 \\ \cline{3-6}  & & 5 & \(-\)43.29 & 24.95 & 74.68 & 18.78 \\ \cline{3-6}  & & 6 & \(-\)61.31 & \(-\)1.85 & 41.01 & \(-\)7.38 \\ \cline{3-6}  & & 3 & 29.05 & 141.05 & 223.39 & 131.16 \\ \cline{3-6}  & & 4 & \(-\)13.73 & 71.95 & 134.82 & 64.35 \\ \cline{3-6}  & & 5 & \(-\)40.16 & 30.07 & 81.84 & 23.92 \\ \cline{3-6}  & & 6 & \(-\)58.10 & 2.90 & 47.77 & \(-\)2.48 \\ \cline{3-6}  & & 3 & 27.56 & 137.84 & 219.88 & 128.43 \\ \cline{3-6}  & & 4 & \(-\)14.95 & 68.99 & 132.07 & 62.04 \\ \cline{3-6}  & & 5 & \(-\)41.12 & 28.32 & 79.84 & 22.35 \\ \cline{3-6}  & & 6 & \(-\)59.09 & 1.32 & 45.59 & \(-\)4.06 \\ \hline \multirow{8}{*}{ADP–DCS} & \multirow{4}{*}{Random} & 3 & 30.80 & 142.79 & 225.73 & 133.11 \\ \cline{3-6}  & & 4 & \(-\)12.14 & 73.28 & 137.20 & 66.11 \\ \cline{3-6}  & & 5 & \(-\)37.66 & 32.15 & 84.52 & 26.34 \\ \cline{3-6}  & & 6 & \(-\)53.37 & 5.11 & 49.71 & 0.48 \\ \cline{3-6}  & & 3 & 31.16 & 143.92 & 226.67 & 133.92 \\ \cline{3-6}  & & 4 & \(-\)11.85 & 73.42 & 137.75 & 66.44 \\ \cline{3-6}  & & 5 & \(-\)37.03 & 32.22 & 84.47 & 26.55 \\ \cline{3-6}  & & 6 & \(-\)52.19 & 5.13 & 50.54 & 1.16 \\ \cline{3-6}  & & 3 & 30.03 & 141.12 & 223.80 & 131.65 \\ \cline{3-6}  & & 4 & \(-\)12.72 & 72.10 & 135.70 & 65.03 \\ \cline{3-6}  & & 5 & \(-\)37.73 & 31.10 & 82.92 & 25.43 \\ \cline{3-6}  & & 6 & \(-\)53.42 & 4.08 & 48.67 & \(-\)0.22 \\ \hline \hline \end{tabular}
* The objective value is given by Equation (3). \(\overline{\mathbf{Z}}\) denotes the average value of \(Z\).  
\end{table} 
Table 3: Experimental results containing average values of j30, j60, and j90 projects for six different algorithms in terms of \(|E|\). Figure 2: Bar chart of the mean values of the NPV corresponding to each job set against each priority rule for ADP in terms of \(|E|\). Table 4 presents the average values of the NPV for the j30, j60, and j90 project sets corresponding to the six models for different levels of NC. As NC increases, the average value decreases for each project size, which is logical because, as the complexity of the network increases, it becomes harder for each algorithm to find the best NPV. We see that EDA and ACO used as priority for MC have increased the NPV. We can also note that EDA variant is much resistant to changes in NC. Similarly, Figures 4 and \(5\) confirm our observation from Table 4.