## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Priority Rule** & \(\mathbf{R}\mathbf{S}\) & \(\mathbf{Z}\) **for j30** & \(\mathbf{Z}\) **for j60** & \(\mathbf{Z}\) **for j90** & **Overall Z** \\ \hline \multirow{8}{*}{ADP} & \multirow{4}{*}{Random} & \(0.20\) & \(-115.18\) & \(113.49\) & \(261.08\) & \(86.46\) \\ \cline{3-6}  & & \(0.50\) & \(-88.25\) & \(241.39\) & \(476.89\) & \(210.01\) \\ \cline{3-6}  & & \(0.70\) & \(-95.49\) & \(258.23\) & \(524.38\) & \(229.04\) \\ \cline{3-6}  & & \(1.00\) & \(-86.94\) & \(268.06\) & \(555.91\) & \(245.68\) \\ \cline{3-6}  & & \(0.20\) & \(-100.47\) & \(144.72\) & \(302.29\) & \(115.51\) \\ \cline{3-6}  & & \(0.50\) & \(-72.93\) & \(276.19\) & \(523.70\) & \(242.32\) \\ \cline{3-6}  & & \(0.70\) & \(-80.76\) & \(281.97\) & \(558.67\) & \(253.29\) \\ \cline{3-6}  & & \(1.00\) & \(-77.59\) & \(277.39\) & \(566.61\) & \(255.47\) \\ \cline{3-6}  & & \(0.20\) & \(-108.06\) & \(107.51\) & \(293.79\) & \(97.75\) \\ \cline{3-6}  & & \(0.50\) & \(-78.70\) & \(232.58\) & \(506.16\) & \(220.01\) \\ \cline{3-6}  & & \(0.70\) & \(-85.32\) & \(252.59\) & \(544.02\) & \(237.10\) \\ \cline{3-6}  & & \(1.00\) & \(-78.35\) & \(268.06\) & \(565.58\) & \(251.76\) \\ \hline \multirow{8}{*}{Random} & \multirow{4}{*}{Random} & \(0.20\) & \(-91.48\) & \(151.22\) & \(309.74\) & \(123.16\) \\ \cline{3-6}  & & \(0.50\) & \(-64.38\) & \(280.27\) & \(528.21\) & \(248.03\) \\ \cline{3-6}  & & \(0.70\) & \(-68.95\) & \(290.07\) & \(567.03\) & \(262.72\) \\ \cline{3-6}  & & \(1.00\) & \(-64.77\) & \(291.74\) & \(583.66\) & \(270.21\) \\ \cline{3-6}  & & \(0.20\) & \(-88.38\) & \(152.33\) & \(310.85\) & \(124.93\) \\ \cline{3-6}  & & \(0.50\) & \(-62.39\) & \(282.69\) & \(532.31\) & \(250.87\) \\ \cline{3-6}  & & \(0.70\) & \(-67.16\) & \(291.13\) & \(569.31\) & \(264.43\) \\ \cline{3-6}  & & \(1.00\) & \(-61.70\) & \(291.63\) & \(585.27\) & \(271.73\) \\ \cline{3-6}  & & \(0.20\) & \(-93.05\) & \(144.51\) & \(302.47\) & \(117.98\) \\ \cline{3-6}  & & \(0.50\) & \(-66.85\) & \(272.76\) & \(518.77\) & \(241.56\) \\ \cline{3-6}  & & \(0.70\) & \(-71.36\) & \(285.45\) & \(560.22\) & \(258.10\) \\ \cline{3-6}  & & \(1.00\) & \(-64.11\) & \(290.88\) & \(582.94\) & \(269.90\) \\ \hline \hline \end{tabular}  
\(\mathbf{Z}\) denotes the average value of \(Z\).  
\end{table} 
Table 6: Average values of the NPV for j30, j60, and j90 projects for six different algorithms in terms of RS. Figure 7: Bar chart of the mean values of the NPV corresponding to each job set against each priority rule for ADP–DCS in terms of RF. From Tables 3-6, we observe the performance of each model is corelated to the parameter under consideration with ADP-DCS using EDA as priority, producing superior results in most of the cases. As the focus of our study is on PEO, we further investigate the effect of different values of \(|E|\) by performing a series of statistical tests to compare their means. Table 7 shows [PERSON]'s normality test for all project sets. In view of the sample size of 480, the [PERSON]'s test is more suitable than the Shapiro-Wilk test. All the \(p\)-values except ADP-random with \(|E|=5\) are less than 0.05. This indicates that the objective values are not normally distributed except for ADP-random with \(|E|=5\). To compare the mean values, we therefore perform a non-parametric test with the Mann-Whitney U test for each case. Figure 8: Mean bar chart of the NPV corresponding to each job set against each priority rule for ADP in terms of RS. Figure 9: Mean bar chart of the NPV corresponding to each job set against each priority rule for ADP-DCS in terms of RS. Table 8 shows the mean difference between ADP and ADP-DCS using MC with random priority. Because all the \(p\)-values are below 0.05, we conclude that there is a statistically significant difference between the mean values of ADP and ADP-DCS. Based on the values in Table 1, it can be concluded that ADP-DCS is better than ADP when MC with random priority is used.