conclusion wip

discussion.tex

-\section{discussion}
+\section{Conclusion and Discussion}
+
+We have presented an algorithm that calculates solutions to the C2 scheduling problem.
+The C2 scheduling problem expresses the scheduling requirements in damage control scenarios on board of navy ships.
+It is a variant of the MSRCPSP, and extends it with resource affinity constraints and user-defined quality functions.
+We use an evolutionary algorithm to search the solution space for schedules that have a good score according to the weighted product of all objectives.
+
+We argue that evolutionary algorithms lend itself well for our problem, because we are not only interested in the best schedule, but also in alternative solutions.
+Evolutionary algorithms calculate sets of solutions, which as a by-product contains the best alternative solutions it has found.
+Furthermore, evolutionary algorithms do not need knowledge about the function they are trying to optimize.
+This is useful for our purpose, because user-defined quality functions can be arbitrary Turing-complete calculations, and there can be no general heuristic that could take advantage of them to guide the search.
 
 \begin{itemize}[noitemsep]
 \item Why no machine learning AI?

introduction.tex

 \section{Introduction}
 \label{sec:introduction}
 
+\todo{Check terminology: capability function should be quality function}
 In this paper we study scheduling for command and control (C2).
 C2 refers to systems, processes and best practices required to coordinate people and machines to cooperatively reach a common goal.
 C2 is especially needed for dynamic situations where fixed business processes would not be able to deal with unexpected events.