HomeTechniques and Tips@RISK: General QuestionsSimulation versus Optimization

2.3. Simulation versus Optimization

Applies to:
@RISK 5.x and newer, Industrial Edition
Evolver, all releases

What's the difference between simulation and optimization? Does a simulation just add stochasticity to an optimal value?

It kind of goes the other way, actually.

Initially, you probably have a deterministic model in mind. If you want to know what choices you should make in a deterministic setting, you use Evolver to do a deterministic optimization.

But it's more common to take that deterministic model and replace some constants with probability distributions. This reflects your best estimates of the effects of chance — events you can't control. These probability distributions are inputs to @RISK. You also identify outputs of @RISK, Excel cells that are the results of your logic, and whose values you want to track in the simulation. Then, you run an @RISK simulation to determine the range and likelihood of outcomes, taking chance effects into account. This can be done in any edition of @RISK.

See also: Risk Analysis has much more about deterministic and stochastic risk analysis.

An optimization asks a higher-level question while still keeping the probabilistic elements: what about the things you can control? What choices can you make that improve your chances of a favorable outcome? You identify in your model the constants that represent choices you can make; these are called adjustable cells. You can place constraints on those cells, and additional constraints on the model if appropriate. Your model still keeps the probability distributions mentioned above for events that are outside your control. Now you run an optimization in the RISKOptimizer menu within @RISK Industrial Edition.

RISKOptimizer starts with one possible set of choices — one set of adjustable cell values — and then runs a simulation to find out the probabilistic range of outcomes if you made those choices. It chooses another set of adjustable cells and runs a new simulation. The optimizer continues this process, making different sets of choices for the adjustable cells and running a full simulation on each set. Some sets of choices have a better outcome than others, as measured by the target you specified for optimization; this guides @RISK in deciding which sets of choices to try because they're more likely to improve the outcome. Every set of choices gets a full simulation.

At the end of the optimization, you have a set of best values for your adjustable cells. These tell you the choices to make so as to maximize your chance of getting the most favorable outcome, based on your target. And the simulation with that set of adjustable cells tells you the range and probabilities of your outcomes.

Last edited: 2016-03-14

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