\section{Generating Bigger Models} 


\begin{figure}[b]
\small
\begin{center}
\begin{tabular}{|c|r|r|r|c|c|} \hline
Model&LOC&Objectives&Risks&Mitigations&Run-Time*\\ \hline
model1.c&  43  &3         &2    &2          &0.0018   \\ \hline
model2.c& 260   &1         &30   &31         &0.0139   \\ \hline
model3.c& 58   &3         &2    &3          &0.0019   \\ \hline
model4.c& 1226   &50        &31   &58         &0.0906   \\ \hline
model5.c&1412    &32        &70   &99         &0.1751   \\ \hline
\end{tabular}
\end{center}
{\small *average over 100 runs (in seconds)}
\caption{Details of Five DDP Models. }
\label{fig:jplModels}
\end{figure}



\begin{figure}[!h]
\scriptsize
\begin{center}
\begin{tabular}{|l|l|r|r|r|r|} \hline
&Model&LOC&Objectives&Risks&Mitigations\\ \hline
\multirow{15}{*}{\begin{sideways}Random-generated Models\end{sideways}} &
model2\_1.c & 264 & 1 & 30 & 31 \\
&model2\_2.c & 502 & 2 & 60 & 62 \\
&model2\_4.c & 978 & 4 & 120 & 124 \\
&model2\_8.c & 1930 & 8 & 240 & 248 \\
&model2\_16.c & 3834 & 16 & 480 & 496 \\
&model4\_1.c & 1233 & 50 & 31 & 58 \\
&model4\_2.c & 2428 & 100 & 62 & 116 \\
&model4\_4.c & 4818 & 200 & 124 & 232 \\
&model4\_8.c & 9598 & 400 & 248 & 464 \\
&model4\_16.c & 19158 & 800 & 496 & 928 \\
&model5\_1.c & 1545 & 32 & 70 & 99 \\
&model5\_2.c & 3053 & 64 & 140 & 198 \\
&model5\_4.c & 6069 & 128 & 280 & 396 \\
&model5\_8.c & 12101 & 256 & 560 & 792 \\
&model5\_16.c & 24165 & 512 & 1120 & 1584 \\ \hline
\end{tabular}
\end{center}
\caption{Details of incrementally bigger randomly-generated DDP Models. }
\label{fig:ddpModels}
\end{figure}



Currently, the number of DDP models generated at JPL is very limited
due to the relatively short period of time these models have been
around and the long time it takes to get the right project people to
meet and discuss requirements, risks, and mitigations.So far we have
access to the five models summarized in \fig{jplModels}.

One aspect to note from these available models is the increase in
size; from 43 lines of code to 1,412. In order to find the best search
method to find the best solution to these increasingly larger models,
more instances need to be explored and therefore an instance generator
is required. Such instance generator has to follow the constraints set
by the model and randomize the values of its variables so the external
validity of studies done using them is not compromised.

For this study we focused on models 2, 4 and 5, and developed our own
random generator of models and artificially grow the original JPL
models by a factor of 2, 4, 8, and 16, obtaining the models in
\fig{ddpModels}. We use these models for comparing our optimization
algorithms.
