## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Priority Rule** & \(|\mathbf{E}|\) & \begin{tabular}{c} **A, \(p\)-Value** \\ **for j30** \\ \end{tabular} & \
\begin{tabular}{c} **A, \(p\)-Value** \\ **for j60** \\ \end{tabular} &
\begin{tabular}{c} **A, \(p\)-Value** \\ **for j90** \\ \end{tabular} \\ \hline \multirow{8}{*}{ADP} & \multirow{4}{*}{Random} & 3 & 1.704, 0.0002 & 4.545, 0.0000 & 5.342, 0.0000 \\ \cline{3-6}  & & 4 & 0.982, 0.0135 & 3.906, 0.0000 & 4.305, 0.0000 \\ \cline{3-6}  & & 5 & 0.541, 0.1641 & 1.985, 0.0000 & 3.292, 0.0000 \\ \cline{3-6}  & & 6 & 0.772, 0.0444 & 1.436, 0.0011 & 2.0934, 0.000 \\ \cline{3-6}  & & 3 & 1.496, 0.0007 & 4.903, 0.0000 & 6.135, 0.0000 \\ \cline{3-6}  & & 4 & 0.856, 0.0277 & 4.824, 0.0000 & 4.627, 0.0000 \\ \cline{3-6}  & & 5 & 0.751, 0.0502 & 2.812, 0.0000 & 3.786, 0.0000 \\ \cline{3-6}  & & 6 & 1.458, 0.0009 & 1.374, 0.0015 & 2.311, 0.0000 \\ \cline{3-6}  & & 3 & 2.066, 0.000 & 4.509, 0.0000 & 5.224, 0.0000 \\ \cline{3-6}  & & 4 & 1.193, 0.0041 & 3.719, 0.0000 & 3.969, 0.0000 \\ \cline{3-6}  & & 5 & 0.828, 0.0325 & 1.923, 0.0000 & 3.291, 0.0000 \\ \cline{3-6}  & & 6 & 1.337, 0.0017 & 1.132, 0.0058 & 1.801, 0.0001 \\ \hline \multirow{8}{*}{Random} & \multirow{4}{*}{Random} & 3 & 1.664, 0.0003 & 5.037, 0.0000 & 6.363, 0.0000 \\ \cline{3-6}  & & 4 & 0.904, 0.0210 & 4.569, 0.0000 & 4.770, 0.0000 \\ \cline{3-6}  & & 5 & 1.332, 0.0019 & 2.635, 0.0000 & 4.140, 0.0000 \\ \cline{3-6}  & & 6 & 1.454, 0.0009 & 1.633, 0.0003 & 2.589, 0.0000 \\ \cline{3-6}  & & 3 & 1.884, 0.0000 & 4.965, 0.0000 & 6.515, 0.0000 \\ \cline{3-6}  & & 4 & 0.923, 0.0189 & 5.059, 0.0000 & 4.849, 0.0000 \\ \cline{3-6}  & & 5 & 1.469, 0.0008 & 2.905, 0.0000 & 3.941, 0.0000 \\ \cline{3-6}  & & 6 & 1.537, 0.0006 & 1.294, 0.0023 & 2.922, 0.0000 \\ \hline \multirow{8}{*}{ACO} & \multirow{4}{*}{EDA} & 3 & 1.603, 0.0003 & 5.111, 0.0000 & 5.366, 0.0000 \\ \cline{3-6}  & & 4 & 0.663, 0.0826 & 5.010, 0.0000 & 4.239, 0.0000 \\ \cline{1-1} \cline{2-6}  & & 5 & 1.394, 0.0013 & 2.679, 0.0000 & 3.862, 0.0000 \\ \cline{1-1} \cline{2-6}  & & 6 & 1.066, 0.0083 & 1.4319, 0.0011 & 2.539, 0.0000 \\ \hline \end{tabular}  
* The \(p\)-value 0.0000 indicates that the value is smaller than five decimal places.  
\end{table} 
Table 8: A non-parametric test using the Mann–Whitney U test between ADP and ADP–DCS for random priority. Table 9 shows the mean differences between ADP and ADP-DCS using MC with EDA priority. Their mean values are statistically significantly different for all j30 instances and for \(|E|=6\) for j60 and j90. For j60 and j90, the \(p\)-value improves as \(|E|\) increases. In this case, ADP-DCS is better than ADP for smaller projects and when \(|E|\) increases for larger projects. Similarly, Table 10 presents the mean difference between ADP and ADP-DCS using MC with ACO prioritization. We see that the \(p\)-values of both j30 and j60 are less than 0.05. This indicates that ADP-DCS with ACO is statistically better than ADP with ACO for j30 and j60. The superiority of ADP-DCS improves for j90 as \(|E|\) increases. Table 11 shows the mean difference between ADP with EDA and ADP-DCS with ACO as their priorities. In many cases, the mean differences are not statistically significant. However, the \(p\)-value improves with increasing value of \(|E|\) for all project groups. Table 12 shows the mean difference between ADP with ACO and ADP with EDA as their priorities. Again, it shows that most of the means of j30 and j90 are not statistically different; however, for j60, the means are different with high statistical significance. In addition, the \(p\)-values decrease as \(|E|\) increases. This suggests that for ADP, using EDA is slightly better for j60 but does not make much of a difference for smaller \(|E|\) for j30 and j90.