In order to be able to explore all the combinations of our 3 systems we had to extend the already exisitng function by another loop that will consider the third system. This means also a higher computing time as we go from n^2 to n^3. In each step the combine.lifetime-Function is being called. This also had to be further expanded to match our needs. With a third option we added more conditions to check the possible combinations of the 3 events with each other. We realized that the dist.Events-function is being called frequently and that the performance can be exhanced by commenting out the plot.timeline-function. This allowed us to use an n.grid of 4 with a calculating time of 10 minutes. Everything above that would run forever.
Based on the already calculated maintenace schedules we extracted first intervention variables. These were then used accordingly in the associated life-cycle assessment functions. For the multi-objective optimization we overwrote these intervention variables to get a variatey of cases that could appear and therefore changing the final results.