RISKOptimizer  
A New Era in Optimization! Breakthrough Product Optimizes Models with Uncertain Factors!Want to get the best out of a risky situation? Look no further than RISKOptimizer, revolutionary new DecisionTools product. RISKOptimizer combines the advanced Genetic Algorithms of Evolver with the power of @RISK's Monte Carlo simulation engine to optimize models that include uncertain, "stochastic" factors. No other package available can provide the solving power of RISKOptimizer! Simulation with Optimization! The RISKOptimizer Advantage RISKOptimizer was developed by the team that brought you @RISK and Evolver. Instead of tying the two separate products together with a clumsy interface, we have integrated them into a unique new product. The result is a package that combines the best aspects of @RISK and Evolver  fast simulations, accurate optimization, intuitive user interface  and is as fast and easy to use as either package! Why RISKOptimizer? Optimization Add Simulation! Here's a simple example: Suppose you have several factories and want to find the best locations to manufacture different products to meet demand in nearby cities. You want to maximize profits and minimize shipping costs. This is a straightforward optimization problem where you want to assign manufacturing volume, by product, to different factories. You begin to set up your model...but then realize that several key factors  shipping costs, demand, manufacturing costs, etc.  are all uncertain. There's no "best" combination of factors; these are all factors outside of your control. Standard optimization programs can't handle uncontrolled factors. Traditionally you would have had to guess at the uncertain factors and hope for the best. But simulation is designed to handle uncertainty, and RISKOptimizer, with builtin simulation, can handle this type of problem easily! Simulation... Add Optimization Suppose you are developing a new product and want to determine whether or not this venture will pay off in the long run. You build a standard spreadsheet model to calculate the profit for this venture. You decide to use @RISK to run a Monte Carlo simulation and start replacing uncertain values with @RISK functions that represent a range of possible values. Then you realize that some of your assumptions are based on using specific vendors and production methods to construct components of your product. There may be other vendors and methods available to you that could save money. It's also possible that some production methods may make shipping costs unattractive. With @RISK alone, you could run multiple simulations and compare results  but did you try every possible combination of inputs? To do that, you would need optimization. But in this case, optimization would need to be combined with simulation, because the results you are optimizing are simulation results! Why Not Use @RISK and Solver or Evolver? How RISKOptimizer Works RISKOptimizer doesn't replace @RISK or Evolver but works with them to solve optimization problems that include uncertainty. And that's a wide variety of problems! Just think of the problems where you need to find optimal solutions. Then, in those problems, identify values which are uncertain. In the past, you might have just guessed at values for these uncertain factors  greatly diminishing the validity of your results. Now you can stop guessing and use RISKOptimizer to generate robust, optimal solutions! Using RISKOptimizer Step 1: Set up the optimization problem (Evolver) Performing these steps is as easy as working with @RISK and Evolver. Step 1: Setting up the Optimization 1) Specify the Target Statistic 2) Select Adjustable Cells RISKOptimizer uses six different solving methods to find the optimal combination of adjustable cells. Different methods are used to solve different types of problems. The six methods are:
3) Define Constraints 4) Set Stopping Conditions
Optimization Stopping Conditions
Simulation Stopping Conditions 5) Set Other Options Step 2: Adding Uncertainty to the Models Models in RISKOptimizer can use any of the 38 probability distribution functions available in @RISK to define uncertainty. A value of 10 in a spreadsheet cell, for example, can be replaced with the @RISK function =RiskNormal(10,2). This would specify that the possible values for the cell are described by a probability distribution with a mean of 10 and a standard deviation of 2. As in @RISK, these functions behave as standard Excel functions. Correlations between probability distribution functions can be specified with any of the @RISK correlation functions including RiskDepC, RiskIndepC and RiskCorrmat. New @RISK Functions These new functions return values that are updated "real time" as a simulation is running and can be used for specifying constraints or displaying simulation results directly in the spreadsheet. These functions can also be used in @RISK (even if RISKOptimizer is not running) once RISKOptimizer has been installed! Step 3: Running an Optimization When optimizing, RISKOptimizer runs a full simulation for each possible trial solution that is generated by the genetic algorithm (GA) optimization engine. In each iteration of the simulation, probability distribution functions in the spreadsheet are sampled and a new value for the target cell is generated. At the end of a simulation, the result for the trial solution is the statistic that you wish to minimize or maximize for the distribution of the target cell. This value is then returned to the optimizer and used by the GAs to generate new and better trial solutions. For each new trial solution, another simulation is run and another value for the target statistic is generated. The result is the trial solution that provides the best answer to your problem! Get Accurate Results...Fast! Do I need @RISK or Evolver? RISKOptimizer Features
Start Optimizing Today! System Requirements:
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