Find the Best Solutions
to any Problem while
Accounting for Uncertainty
Wouldn’t you like to know the best allocation of your limited resources to maximize your profits? Or the most efficient schedule to minimize costs? But what about the uncertainty inherent in sales projections, returns from individual investments, or production costs?
Traditional optimization methods ignore this uncertainty, a very risky approach. RISKOptimizer tells you not only the best combination of inputs to use, but the risk associated with each strategy. You can seek out strategies that enable you to minimize your risks while achieving your goals.
RISKOptimizer combines the Monte Carlo simulation technology of @RISK, Palisade’s risk analysis add-in, with the latest solving technology to allow the optimization of Excel spreadsheet models that contain uncertain values. Take any optimization problem and replace uncertain values with @RISK probability distribution functions that represent a range of possible values. For each trial solution RISKOptimizer tries during optimization, it runs a Monte Carlo simulation, finding the combination of adjustable cells that provides the best simulation results.
RISKOptimizer is fully integrated with @RISK Industrial in the Tools ribbon group. (@RISK Industrial is available by itself or as part of the DecisionTools Suite Industrial.)
RISKOptimizer also now has the ability to share simulation settings with @RISK, saving redundant entry. All RISKOptimizer commands are available directly from the @RISK ribbon. Best of all, all the @RISK reports, graphs and features are available to analyze RISKOptimizer’s best solution.
In addition, RISKOptimizer now handles constraints more efficiently, making optimizations faster. It also supports discrete adjustable cells, better reflecting reality in many models and further speeding up optimizations by not wasting time on impossible scenarios.
How RISKOptimizer Works
Standard optimization programs are good at finding the best combination of values to maximize or minimize the outcome of a spreadsheet model given certain constraints. However, these programs are not set up to handle “uncontrolled” uncertainty, and require static values for any factor that is not being adjusted by the optimization. This forces modelers into making decisions based on overly simplistic or inaccurate results.
Add Simulation to Optimization
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. But key factors out of your control are uncertain: shipping costs, demand, etc. Traditionally you would have had to guess at the uncertain factors and hope for the best. With RISKOptimizer, those uncertain factors are represented with probability distribution functions (like Normal, Triang, etc.) so that a Monte Carlo simulation can be run for each trial allocation of manufacturing volume. In this way, you can maximize the mean of the simulated output – say profits – an account for risk during optimization.
@RISK uses Monte Carlo simulation to account for the uncertainty in models and determine the probability of various outcomes occurring. But Monte Carlo simulation does not deal with decision variables whose values you can control. It handles random, uncertain values at a single state of those decision variables.
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, replacing uncertain factors like demand and material costs with @RISK functions. Then you realize that some of your assumptions are based on using specific vendors and production methods to construct 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? With RISKOptimizer, you can try different combinations of vendors and methods to maximize your profits.
Using RISKOptimizer involves three simple steps:
1. Set Up Your Model.
The RISKOptimizer Model window provides one-stop setup for all optimization problems. Here you specify the target cell and statistic, identify cells to adjust, and define constraints. Adjustable cells and constraints support cell ranges for easy setup and changes, while target cells can be maximized, minimized, or approach a specific goal.
When defining adjustable cells, you can specify the maximum and minimum boundaries of ranges of cells directly in Excel, greatly simplifying setup and making changes easy. For example, you can tell RISKOptimizer to adjust cells B1:B5, with a minimum value for each in A1:A5, and a maximum value for each in C1:C5. Multiple groups of cells may be specified, with multiple ranges in each group.
You must also define constraints in your model. For example, there may be limited resources which must be modeled. When defining constraints (hard or soft), you can also specify minimums and maximums with cell ranges.
Finally, set stopping conditions for your optimization, telling RISKOptimizer when to stop each simulation and when to halt the optimization as a whole.
RISKOptimizer uses six different solving methods that you can specify to find the optimal combination of adjustable cells. Different methods are used to solve different types of problems. The six methods are:
- Recipe - a set of variables which can change independently.
- Grouping - a collection of elements to be placed into groups.
- Order - an ordered list of elements.
- Budget - recipe algorithm, but total is kept constant.
- Project - order algorithm, but some elements precede others.
- Schedule - group algorithm, but assign elements to blocks of time while meeting constraints.
In your spreadsheet itself, you need to add probability distribution functions to describe uncertain factors beyond your control. For more on probability distribution functions, see @RISK.
RISKOptimizer also allows a great degree of control over how it performs the optimization itself. You can set optimization and simulation parameters, runtime settings, control macros, and more in the RISKOptimizer Settings dialog.
2. Run the Optimization.
Click the Start icon to start the optimization. RISKOptimizer will start generating trial solutions, and running Monte Carlo simulations on each one, in an effort to achieve the target set in Step 1. The summary RISKOptimizer Progress window appears, showing simulation status and best answer achieved thus far. This window lets you pause, stop, and run the optimization using playback controls. You can also monitor progress in detail with the RISKOptimizer Watcher. Tabbed reports show real-time updates on best answers achieved, all solutions tried, the diversity of solutions being tried, and more.
What Optimization Does
During an optimization, RISKOptimizer generates a number of trial solutions and uses genetic algorithms to continually improve results of each trial. For each trial solution, a Monte Carlo simulation is run, sampling probability distribution functions and generating a new value for the target cell - over and over again. The result for each trial solution is the statistic that you wish to minimize or maximize for the output distribution of the target cell (mean, standard deviation, etc.). For each new trial solution, another simulation is run and another value for the target statistic is generated.
3. View Optimization Results.
After optimization, RISKOptimizer can display the results of the original, best, and last solution on your entire model, updating it with each scenario in a single click. This makes it easy to decide the best course of action. You can also generate reports directly in Excel for an optimization summary, log of all simulations, and log of progress steps.
Excel Ease of Use
RISKOptimizer is a true add-in to Microsoft Excel, integrating completely with your spreadsheet. Define your models, adjust your settings, run optimizations, monitor progress, and generate reports – while never leaving Excel. Streamlined dialog boxes mean fewer open windows to navigate.
Part of the DecisionTools Suite
RISKOptimizer is part of the DecisionTools Suite Industrial, Palisade’s complete risk and decision analysis toolkit. The DecisionTools Suite includes @RISK for risk analysis with Monte Carlo simulation, NeuralTools for prediction using neural networks, StatTools for statistical analysis, PrecisionTree for decision trees, TopRank for what-if analysis, and more. RISKOptimizer can be combined with DecisionTools Suite programs for greater insight and analysis. For example:
RISKOptimizer and StatTools
You could run a RISKOptimizer analysis on the results from a StatTools time-series forecast, applying @RISK functions to the forecasted values while adjusting controllable factors to maximize total profits.
Combine RISKOptimizer with NeuralTools to make live predictions on each trial solution.
Licensing and Training
@RISK is available through a variety of licensing options, including corporate, network, and academic licenses. Training, consulting, and books can be bundled with your software to ensure you and your staff get the most out of your investment.
@RISK simulations are calculated 100% within Excel, supported by Palisade sampling and statistics proven in over twenty years of use. Palisade does not attempt to rewrite Excel in an external recalculator to gain speed. A single recalculation from an unsupported or poorly reproduced macro or function can dramatically change your results. Where will it occur, and when? Palisade harnesses the power of multiple CPUs and multi-core processors to give you the fastest calculations. Correct results-and fast-using @RISK!
COMPATIBILITY: @RISK and DecisionTools Suite software is compatible with all 32-bit and 64-bit versions of Microsoft Windows XP-8, Excel 2003-2013, and Project 2003-2013.
64-bit technology enables Excel and DecisionTools software to access much more computer memory than ever before. This allows for vastly larger models and greater computational power.