@@ -23,6 +23,14 @@ 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{Influence schedule task ordering}
In the current implementation, the evolutionary algorithm calculates an assignment, from which a schedule is built in a greedy manner, where tasks first in the instance definition get scheduled first.
This way, task priorities can be modelled by putting tasks with higher priority earlier in the instance definition.
The downside is that the scheduler and the quality functions have no influence on the task ordering.
It would be nice to be able to let the quality functions evaluate the task ordering.
That would make it possible for example to encode dynamic blanket search routes as a scheduling problem.
This is a difficult extension to the algorithm however, as the chromosomes need to encode a task ordering that should be guaranteed to respect the ordering imposed by the ordering constraints of the instance.
\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.
