## 5 Discussion  
The authors of [70] mention that the parameters considered for generation of projects from _ProGen_ reflect real-life practical projects. In this study, having made a comprehensive analysis based on all important parameters, the observations and conclusions made above \begin{table}
\begin{tabular}{c c c c} \hline \hline \(|\)**E\(|\)** & _W, p-Value j30_ & _W, p-Value j60_ & _W, p-Value j90_ \\ \hline
3 & 119,980, 0.2658 & 120,907, 0.2252 & 119,037, 0.3718 \\ \hline
4 & 120,758, 0.1957 & 119,508, 0.3745 & 118,965, 0.3808 \\ \hline
5 & 121,192, 0.1631 & 119,678, 0.3535 & 118,796, 0.4025 \\ \hline
6 & 125,314, 0.0185 & 120,348, 0.2787 & 120,376, 0.2283 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Non-parametric test using the Mann–Whitney U test between ADP–DCS with ACO and ADP–DCS with EDA as their priorities. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(|\)**E\(|\)** & _W, p-Value j30_ & _W, p-Value j60_ & _W, p-Value j90_ \\ \hline
3 & 120,671, 0.2028 & 134,082, 0.0000 & 120,046, 0.2593 \\ \hline
4 & 122,326, 0.0971 & 136,325, 0.0000 & 120,164, 0.2478 \\ \hline
5 & 122,504, 0.0890 & 137,435, 0.0000 & 120,058, 0.2581 \\ \hline
6 & 124,479, 0.0308 & 141,544, 0.0000 & 121,506, 0.1422 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Non-parametric test using the Mann–Whitney U test between ADP with ACO and ADP with EDA as their priorities. hold for project sizes between 30 and 90 jobs. That is, for each parameter we analyzed, the performances of different models by varying values of parameters of interest resulted in a sensitivity analysis. We observe that in all instances, ADP(-DCS)-EDA consistently outperforms other models, indicating its robustness. Our statistical analysis performed on varying cardinalities of \(E\) also confirms ADP(-DCS)-EDA's superiority. We must also note that by nature of our experimental design, the hybrid methods proposed are theoretically superior to their standalone counterparts. Our analysis indicates that the approximation of policy rule using distribution estimated by EDA is better than random approximation and approximation by ACO. It can also be seen that policy approximation by ACO is relatively better in most cases than random approximations. However, considering the NP-hard classification of the problem at hand, the results obtained may not represent the theoretical optimum, suggesting that there is room for the further refinement and improvement of these values. Furthermore, the stochastic nature of the algorithms under consideration means that repeated executions may lead to slightly varying results. It is also important to note that no comparison of computation times was made in this study, as such metrics are significantly affected by the computational capacity of the processing machines used. Moreover, because our experiment involved j30, j60, and j90, the results may not hold true for larger problem sets (projects with more than 90 jobs) or smaller projects (less than 30 activities). An analysis of these models on larger projects remains out of the scope of this study but is reserved for the future. This study offers numerous practical implications. First, as RCPSPDC-PEO is the most logical mode of agreement a contractor and a client can enter, practitioners can employ our models off-the-shelves to maximize their profitability. As indicated in the introduction, these models have multitude of advantages, including the capacity to execute parallelly on multiple cores of computing systems. This not only enhances computational efficiency but also makes our method accessible and scalable. Our models also offer customization capabilities to meet the needs of practitioners. For instance, an user can only employ the ADP part of the model in a quicker manner to determine a reasonably good schedule. Customizable models like ours are also important tools for an agile project management, which is prevalent in innovative product development.