Each optimization model has one objective that mathematically represents the model’s goal as a function of the assumption and decision variable cells, as well as other formulas in the model. OptQuest’s job is to find the optimal value of the objective by selecting and improving different values for the decision variables.
When model data are uncertain and can only be described using probability distributions, the objective itself will have some probability distribution for any set of decision variables. You can find this probability distribution by defining the objective as a forecast and using Crystal Ball to simulate the model.