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\begin{document}

\bibliographystyle{alpha}
\begin{center} 
{\LARGE\bf 
Plan (September to December 1999): Dissertation, Papers, and Work
}
\vspace{1cm}

{\large
Saleh Elmohamed,\\}
\vspace{.5cm}
Northeast Parallel Architectures Center\\
Syracuse University, Syracuse, NY 13244\\
\vspace{.25cm}
saleh@npac.syr.edu\\
\end{center}

\vspace{1cm}
\centerline{\today}

\vspace{1.5cm}
\centerline{ABSTRACT}
The centerpiece of this plan is the completion and defense
of the dissertation. In addition, a journal quality paper on
optimization to be completed no later than a month from today,
around mid October. In regard to the non-optimization work, Sang Lim
and I will shortly be integrating an N-body applet, originally written
by Ernie Sibert and his students and later updated by me, into
mpiJava. A demo using this applet for SC99 will be put
together. Second, Dongmin Kim and I are integrating the CFD applet 
into Tango. We've  already started this work and hope to complete it
soon. Other applets, such as the 2D Ising Model, are also planned to
be integrated afterward.


\vspace{4cm} 
% \vfill

\vfill

\newpage
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Some Details of the Plan}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\setcounter{page}{1}

This is my roadmap to completion of my PhD, putting together two
journal quality papers, and finding a good job.
\begin{itemize}

\item Dissertation: {\bf Algorithms and Heuristics for Combinatorial Optimization
Problems: Case Study of Academic Course Scheduling}

In this dissertation we try to answer the following question: How do
we go about solving optimization problems, in general, and academic
course scheduling (or timetabling) at the university level, in
particular? We give a detailed answer to the latter part, and shed
some light on the case of tackling optimization problems in
general. To summarize, we have investigated a variety of optimization
problems, in particular, random search, hill climbing, knowledge-based
systems (or expert systems), tabu search, and simulated
annealing-based methods to solve the course scheduling problem. The
simulated annealing-based approach includes mean-field annealing,
simulated annealing with three different cooling schedules, and the
use of a rule-based preprocessor to provide a good initial solution
for annealing. The best results were obtained using simulated
annealing with adaptive cooling and reheating as a function of cost,
and a rule-based preprocessor. This approach enabled us to obtain
valid schedules for the timetabling problem for a large university,
using a complex cost function that includes student preferences. None
of the other methods were able to provide a complete valid schedule.

\item Paper: {\bf A Comparison of Annealing Techniques for Academic Course
    Scheduling}

The first version appeared in Springer Lecture Notes in Computer
Science, vol 1408. The second version includes more details and
analysis of the results, such as plots and figures showing the
behavior of the annealing schedules used and the effects of the
various constraints involved on such behavior.

\item Paper: {\bf Course Scheduling using Mean-Field Annealing -- Algorithm
    Analysis and Implementation}

This paper is in the final stage of completion. The only things that
need to be included are few tables of data and plots of some of the
obtained results from the experiments.

\item Memo: {\bf Academic Scheduling using Simulated Annealing with a
    Rule-Based Preprocessor}

\item Project (programming + paper): {\bf Integration of Java-based
    Educational Resources with the Collaboratory System Tango: Case
    study of CFD and 2D Ising Model simulation} 

The CFD and Ising Models are two applets that were designed
specifically for educational purposes. They include features allowing
the user, for example, to control the flow based on density, velocity,
temperature, etc. in the former and the energy per spin as well as
magnetization in the latter. In the case of the Ising model the user
can specify the temperature with respect to the critical temperature,
and in the CFD case the 2nd and 4th order damping of the flow can
easily be changed throughout the simulation.

The integration will have two modes: master-master and
master-slaves. In an educational setting the master will be the
instructor and the slaves are the students. In the first mode, the
students will have as much control over the simulation as the
instructor while in the second mode the instructor will have a higher
degree of control. Also, in the framework of the second mode there are
instances when the instructor will allow or authorize one or more
students to have more control over the simulation. For example, in the
case of a student having a question about certain aspect(s) of the
simulation, the student needs to have some control in order to show
the points that s/he is asking about. Furthermore, the rest of the
class, including the instructor, also need to be able to see what the
student is asking about. 
In a collaborative system such as tango there may be other modes of
control that need to be investigated in the framework of this
integration.

\item Project (programming,etc.):
{\bf mpiJava for Scientific and Engineering Applications: Case
    study of the N-body problem}

For SC98, CFD simulation was shown as an example of a large scale
engineering application that can be done in mpiJava. For SC99, we
(Sang, Bryan, and Saleh) will implement the N-body problem in
mpiJava. If all goes well, a demo of this implementation will be shown
during the conference.
\end{itemize}

\end{document}

