Commit 28eb9bc8 authored by Markus Klinik's avatar Markus Klinik
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explain damage control scenario

parent 338a6354
......@@ -70,5 +70,25 @@ The fact that in \cref{fig:conflict7} task t12 is assigned to r2 instead of r3 i
\subsection{Crew Location}
\label{sec:example-crew-location}
This example demonstrates a scenario where many tasks need to be executed.
The scenario is based on an example by \citet{Kool2017}.
This example shows a scenario where many different tasks must be scheduled in a damage control situation on board of a navy ship.
There are routine tasks that are interspersed with damage control tasks.
The scenario is based on an example by \citet{Kool2017}, extended with several tasks that demonstrate the features of our method.
The scenario has five lines of work that must be completed.
A weapon needs to be readied and used, which consists of five tasks.
A meeting with the high ranking officers must be scheduled.
A cook must prepare food.
The deck is divided into 15 sectors that must all be swabbed.
In addition to this routine work, there has been a grenade impact, and now blanket search must be carried out in order to assess the damage.
Blanket search consists of one inspection task per room on the ship.
The weapon tasks demonstrate the resource affinity feature of our algorithm.
The scheduler must choose the same weapon for the preparation and usage tasks.
The blanket search tasks demonstrate user-defined capability functions.
We assume a simple model of the ship's layout, where 30 rooms are located on one long corridor.
All crew members are randomly distributed across the first ten rooms.
The quality function evaluates people assigned to blanket search tasks according to the distance between the person and the room, where distance is a less-is-better objective.
Calculating the distance between a person and a room in this model is a simple subtraction, but one could imagine a more realistic model where distances are calculated using a model of an actual ship with corridors and staircases.
This example wants to show where this kind of computation fits into the scenario.
The cooking, swabbing and meeting tasks are there to provide some noise for the scheduler.
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