Commit 2a817338 authored by Markus Klinik's avatar Markus Klinik
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future work: HITL and other methods

parent 12da8154
\section{Evolutionary Algorithms}
\label{sec:evolutionaryAlgorithms}
\label{sec:evolutionary-algorithms}
Why are off-the-shelf schedulers not applicable? \citet{Deb2011}
......@@ -14,7 +14,19 @@ If we could calculate some sort of \emph{degree of similarity} between two sched
Candidates that are more similar to the running schedule should be preferred.
As with all other objectives, this similarity measure should be weighted so that users can influence the algorithm to either calculate a faster but disruptive new schedule, or one that does not disrupt the current activities but takes longer, or has lower quality.
\begin{itemize}[noitemsep]
\item Study other population-based meta-heuristics
\item Human-in-the-loop.
\end{itemize}
\paragraph{Human in the loop}
The long-term goal for this work is the development of interactive decision support, which is integrated into a mission management tool.
The decision maker should always have the last word, and the tool should allow all decisions to be manually overridden, even if that violates constraints.
It is always possible that the constraints are erroneous, for example that a resource can actually be assigned to a task, even if that is not known to the tool.
In such a case, humans should not be restricted by the design of the software.
This is called \emph{human in the loop}.
Future work in this direction should first identify requirements in which ways humans should be able to change the outcome of the scheduler, and then implement it in a user-friendly and intuitive manner.
This could include visualization of the consequences to resources and running tasks of manual overrides.
\paragraph{Other population-based methods}
We argued in \cref{sec:evolutionary-algorithms} that evolutionary algorithms are a natural fit for our problem, because they are population-based metaheuristics.
We chose an evolutionary algorithm for our proof-of-concept implementation because it is easy to implement and extend.
There are other population-based metaheuristics in the literature that could be explored as well, like particle swarm optimization or ant colony optimization.
This line of work would require developing a set of example instances that covers many of our intended scenarios and takes our implementation to the computational limit.
Then, the other methods have to be implemented as solvers to the C2 scheduling problem.
In this way, it would be possible to experimentally compare which metaheuristics is faster and delivers better results.
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