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Unless performance measures are part of a constraint or an objective, OptQuest does not use those values as part of its search. Those values are recorded by FlexSim, but they don't influence the optimization.

Usually, however, performance measures are part of an objective function. This is where they will influence the OptQuest search. Often, an objective function might simply return a performance measure value. Then the user must tell OptQuest whether to minimize or maximize that value.

This brings me to your questions about multi-objective optimization. In FlexSim, there are two ways that you can use multiple objectives. The recommended way is to use the Pattern search option. The pattern search will show the pareto frontier, which shows the set of solutions that have the best trade-offs. For example, if you maximize revenue, and minimize cost, the pareto frontier will show you a range of solutions. The first solution will show the cheapest, lowest revenue option, completely favoring the cost objective. The last solution will show the most expensive, highest revenue option, completely favoring the revenue objective. For every other solution on the pareto frontier, you can know that there is no solution that is cheaper that give as good a revenue.

In weighted mode, OptQuest simply adds together all the objective functions (multiplied by their weights), and maximizes the result. You could manually do the same in a single objective, if you wanted. However, this search method is not recommended; correctly setting the weights is very difficult. It is much easier (and often much more informative) to have the optimizer show you the trade-off curve, and then allow you to choose the balance between objectives that you'd prefer.