Most simulation models have variables that you can control, such as how much to charge for rent or how much to invest. In Crystal Ball, these controlled variables are called decision variables. Finding the optimal values for decision variables can make the difference between reaching an important goal and missing that goal.
Obtaining optimal values generally requires that you search in an iterative or ad hoc fashion. A more rigorous method systematically enumerates all possible alternatives. This process can be very tedious and time consuming even for small models, and it is often not clear how to adjust the values from one simulation to the next.
OptQuest overcomes the limitations of both the ad hoc and the enumerative methods by intelligently searching for optimal solutions to your simulation models. You describe an optimization problem in OptQuest and then let it search for the values of decision variables that maximize or minimize a predefined objective. In almost all cases, OptQuest will efficiently find an optimal or near-optimal solution among large sets of possible alternatives, even when exploring only a small fraction of them.
The easiest way to understand what OptQuest does is to apply it to a simple example. Tutorial 1 — Futura Apartments Model demonstrates basic OptQuest operation.