You can significantly improve OptQuest’s performance by selecting meaningful bounds for the decision variables. Suppose, for example, that the bounds for three variables (X, Y, and Z) are:
And in addition to the bounds, there is the following constraint:
Although the optimization model is correct, the variable bounds are not meaningful. A better set of bounds for these variables would be:
These bounds take into consideration the values of the coefficients and the constraint limit to determine the maximum value for each variable. The new "tighter" bounds result in a more efficient search for the optimal values of the decision variables.
Since constraints limit the decision variables you are optimizing, OptQuest can eliminate sets of decision variable values that are constraint-infeasible before it spends the time running the simulation. Therefore, limiting the optimization with constraints is very time-effective.