## 5 Simulation Result
### Fictitious Project Results
#### 5.1.1 Effect of Input Parameters on the Simulator Results  
The sensitivity analysis of some important input parameters is presented in this section. Resource constraints, work contours and changes in the progress status are considered as changing factors, and their effects on simulator outputs are studied.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MAX** & **RMSE** & **R** \\ \hline ACWP & 156,015.81 & & \\ SSA & 158,153.98 & 1050.64 & 0.92 \\ S-Curve & 155,155.24 & 2166.50 & 0.93 \\ \hline \end{tabular}
\end{table}
Table 1: Deviations between SSA trend and the S-curve method with the generated time series.  
Figure 15: Deviations between the SSA trend and the S-curve method with the generated time series.  
* Effect of Resource Constrains:  
Taking into consideration resource constraints, both the project time and cost will increase. Figure 16 shows the resulting S-curve of the project for both with and without resource leveling.  
Resource leveling lengthens the project completion time 40 days and increase the project cost by about $9500. Moreover, simulator output in the case of resource leveling fluctuates considerably. These two time series have been analyzed by SSA. The window length is set to half of the project duration (L = 53). Figure 17 shows the principal components related to the first three eigentriples of the resulting time series.  
The two first principal components have approximately the same behavior, and in fact, both series have a similar trend. However, the third principle components of new time series have a different structure due to the high fluctuation of the series. In the case of resource leveling, based on w-correlations analysis, no harmonic components are detected in the resulting time series and residuals, which are obtained by the eigentriples 4 to 53 fluctuating more.  
Figure 16: Simulator output with and without resource leveling. AC, Actual Cost.  
Figure 17: Principal components related to the first three eigentriples.  
* Effect of discipline and work contour:  
In this section, the simulator is run in two different settings of work contour: front loaded and back loaded and with the assumption of resource leveling. Principal components related to the first three eigentriples of the resulting time series are presented in Figure 18.  
In this case, the two first components of the resulted series are similar, but the third component has a different shape. Besides, the third component has more fluctuation in the front-loaded work contour setting than the back-loaded one.  
Although the residuals of these time series have various behaviors and structures, these series have a similar harmonic component, as well.  
* Effect of the changes in the progress status:  
To evaluate the impacts of activities' progress status on actual progress and the simulator output, the simulator is run in two various statuses:  
* The actual progress of tasks fluctuates \(\pm\)0.2 of their plan progress.
* The actual progress of tasks fluctuates \(\pm\)0.3 of their plan progress.  
Moreover, it is assumed that the project has a flat work contour and resource leveling. When the interval range increases, project completion time and cost raise, while project progress fluctuates more.  
Figure 19 presents the first three eigentriple components of the resulting time series. The increase of this range results in a large fluctuation in the time series. This is apparent in the second and third principle components. However, the first principle component of the two series has approximately the same structure, and therefore, the two series have generally similar trends.  
Figure 18: Principal components related to the first three eigentriples of the time series in two different settings: front-loaded and back-loaded work contour.