## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Priority Rule** & **NC** & \(\overline{Z}\) **for j30** & \(\overline{Z}\) **for j60** & \(\overline{Z}\) **for j90** & **Overall \(\overline{Z}\)** \\ \hline \multirow{6}{*}{ADP} & \multirow{3}{*}{Random} & \(1.5\) & \(-88.82\) & \(247.42\) & \(511.69\) & \(223.43\) \\ \cline{3-7}  & & \(1.8\) & \(-97.04\) & \(217.01\) & \(443.85\) & \(187.94\) \\ \cline{3-7}  & & \(2.1\) & \(-103.53\) & \(196.45\) & \(408.15\) & \(167.02\) \\ \cline{3-7}  & & \(1.5\) & \(-73.86\) & \(274.41\) & \(548.44\) & \(249.66\) \\ \cline{3-7}  & & \(1.8\) & \(-83.38\) & \(242.47\) & \(476.26\) & \(211.78\) \\ \cline{3-7}  & & \(2.1\) & \(-91.56\) & \(218.32\) & \(438.76\) & \(188.51\) \\ \cline{3-7}  & & \(1.5\) & \(-78.57\) & \(240.97\) & \(536.95\) & \(233.12\) \\ \cline{3-7}  & & \(1.8\) & \(-87.90\) & \(211.98\) & \(467.86\) & \(197.31\) \\ \cline{3-7}  & & \(2.1\) & \(-96.34\) & \(192.60\) & \(427.35\) & \(174.54\) \\ \hline \multirow{6}{*}{ADP–DCS} & \multirow{3}{*}{Random} & \(1.5\) & \(-62.48\) & \(283.69\) & \(558.86\) & \(260.02\) \\ \cline{3-7}  & & \(1.8\) & \(-72.34\) & \(250.34\) & \(485.65\) & \(221.22\) \\ \cline{3-7}  & & \(2.1\) & \(-82.35\) & \(225.94\) & \(446.97\) & \(196.85\) \\ \cline{3-7}  & & \(1.5\) & \(-59.62\) & \(286.08\) & \(561.93\) & \(262.80\) \\ \cline{3-7}  & & \(1.8\) & \(-69.97\) & \(251.91\) & \(487.49\) & \(223.14\) \\ \cline{3-7}  & & \(2.1\) & \(-80.13\) & \(226.09\) & \(448.88\) & \(198.28\) \\ \cline{3-7}  & & \(1.5\) & \(-63.90\) & \(279.09\) & \(552.46\) & \(255.88\) \\ \cline{3-7}  & & \(1.8\) & \(-73.65\) & \(245.00\) & \(480.23\) & \(217.19\) \\ \cline{3-7}  & & \(2.1\) & \(-83.97\) & \(221.09\) & \(440.60\) & \(192.57\) \\ \hline \hline \end{tabular} \(\overline{Z}\) denotes the average value of \(Z\).  
\end{table} 
Table 4: Average values of the NPV for j30, j60, and j90 projects for six different algorithms in terms of NC. Figure 3: Bar chart of the mean values of the NPV corresponding to each job set against each priority rule for ADP–DCS in terms of \(|E|\). Table 5 presents the average values of the NPV for the j30, j60, and j90 project sets corresponding to the six models for different levels of RF. The efficacy of all algorithms decreases as the RF increases except for EDA. Again, we see that EDA and ACO used as priority for MC have increased the NPV. Figures 6 and 7 again show that ADP(-DCS)-EDA demonstrates superior performance over other methods in terms of RF. Table 6 shows the average values of the NPV for the j30, j60, and j90 project sets corresponding to the six models for different levels of RS. As the RS increases, the average values of all algorithms increase. In this case, we see that APD_DCS using MC with random priority generated better solutions than with ACO as priority; however, EDA maintained its superiority. The robustness of ADP(-DCS)-EDA in terms of RS is confirmed by Figures 8 and 9 as it consistently outperforms other methods in all different project sizes. Figure 4: Bar chart of the mean values of the NPV corresponding to each job set against each priority rule for ADP in terms of NC. Figure 5: Bar chart of the mean values of the NPV corresponding to each job set against each priority rule for ADP–DCS in terms of NC. Figure 6: Bar chart of the mean values of the NPV corresponding to each job set against each priority rule for ADP in terms of RF.