In an applied project we are working on currently, we want to use robust or stochastic programming in order to enhance the performance of the systems (by reference to certain metrics). As you may already know, robust/stochastic optimizations allow to model the randomness impact of certain factors on a system behavior (in my case, its performance).
On the other hand, solving robust/stochastic problems is costly in time/resources or both: my question is how frequently the problem needs to be solved in order to provide up-to-date paramaters for the system to operate at maximized/required performance? What would be the best/most efficient interaction scenario for the system to trigger the problem solving process?
Any feedback on previous experience or similar work would be very appreciated.