RISKOptimizer uses genetic algorithms or OptQuest methods, along with Monte Carlo simulation, to solve optimization problems under uncertainty. Take any optimization problem and replace uncertain values with @RISK functions that represent ranges of possible values. RISKOptimizer will try different combinations of adjustable cells to achieve the goal you define, while running Monte Carlo simulations on each trial solution to account for inherent uncertainty. The result is the most robust, accurate solution possible.

Greater Than the Sum of its Parts  Each component of the DecisionTools Suite can perform a powerful analysis. When you combine these products, you can achieve more complete results than any single program can provide.

RISKOptimizer and @RISK  Run RISKOptimizer on an existing @RISK model to maximize your profits, minimize your costs, or achieve a particular target. RISKOptimizer uses all the same functions as @RISK.

RISKOptimizer and StatTools  You could run a RISKOptimizer analysis on the results from a StatTools forecast, applying @RISK functions to the forecasted values while adjusting controllable factors to maximize total profits.

RISKOptimizer and NeuralTools  Even combine RISKOptimizer with NeuralTools to make live predictions on each trial solution