Rockwell Automation Arena OptQuest Users Guide - Manual (Page 47)

5 • O PTIMIZATION T IPS AND S UGGESTIONS
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Run the optimization to get an approximate solution.
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Add the approximate solution as a suggested solution.
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Further restrict the bounds on the controls.
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Increase the number of replications to increase accuracy.
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Rerun the optimization.
You might deselect controls that don’t seem to have an influence on the value of the
objective. When you deselect one or more controls and rerun the optimization, the search
focuses on the remaining, more important, controls.
Initial values
The initial values are the values listed in the Suggested Values column of the Controls
Summary window.
The initial values are important because the closer they are to the optimal value, the faster
OptQuest can find the optimal solution. If the initial values are constraint-infeasible, they
will be ignored.
For potentially large models with many controls, you might find it helpful to first run a
simplified version of the optimization (for example, by using expected values for some of
the random variables in the model) to find initial values for the full-blown model.
Suggested solutions
Suggested solutions are listed in the Suggested Solutions window. These may be solutions
saved from a previous optimization or they may be solutions you have entered.
Suggested solutions are always evaluated first in an optimization. The closer they are to
the optimal value, the faster OptQuest can find the optimal solution.
Bounds and constraints
You can significantly improve OptQuest’s performance by selecting meaningful bounds
for the controls. Suppose, for example, that the bounds for three controls (X, Y, and Z)
were:
0 <= X <= 100
0 <= Y <= 100
0 <= Z <= 100
And in addition to the bounds, there is the following constraint:
10*X + 12*Y + 20*Z <= 200