Commit 01b16cbe authored by Markus Klinik's avatar Markus Klinik

conclusion wip

parent 0f5e950b
\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.
\item Why no machine learning AI?
\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.
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