RISKOptimizer

Simulation with Optimisation

Available with @RISK Industrial or the DecisionTools Suite Industrial, RISKOptimizer is the simulation optimisation add-in for Microsoft Excel. RISKOptimizer combines the Monte Carlo simulation technology of @RISK, Palisade's risk analysis add-in, with genetic algorithm optimisation technology to allow the optimisation of Excel spreadsheet models that contain uncertain values. Take any optimisation problem and replace uncertain values with @RISK probability distribution functions that represent a range of possible values. For each trial solution RISKOptimizer tries during optimisation, it runs a Monte Carlo simulation, finding the combination of adjustable cells that provides the best simulation results.

All-new RISKOptimizer

RISKOptimizer has been re-engineered from the ground up in a stunning new version. A streamlined interface, full support for cell ranges, enhanced monitoring of optimisation progress, a faster engine and more make RISKOptimizer PC World's "most practical power tool around."

RISKOptimizer Industries and Applications

RISKOptimizer is used to solve optimisation problems in a wide range of industries, from finance to airlines to manufacturing.

  • Airlines and hotels - E.g. Yield management with uncertain demand
  • Manufacturing - E.g. Job shop, production, manpower scheduling; Inventory management; Capacity planning; Product mix decisions
  • Finance - E.g. Portfolio maximisation; Hedging strategies; Portfolio balancing; Retirement planning
  • General - E.g. Market entry timing; Bidding decisions
  • Retail - E.g. Optimisation of ordering policies

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 optimisations, monitor progress, and generate reports - while never leaving Excel. Streamlined dialog boxes mean fewer open windows to navigate.

Why RISKOptimizer?

Standard optimisation programs are good at finding the best combination of values to maximise or minimise 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 optimisation. This forces modelers into making decisions based on overly simplistic or inaccurate results.

Add Simulation to Optimisation - 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 maximise profits and minimise shipping costs. This is a straightforward optimisation 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 maximise the mean of the simulated output - say profits - an account for risk during optimisation.

Add Optimisation to Simulation - @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 realise 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 maximise your profits.

Optimisation 1-2-3

Using RISKOptimizer involves three simple steps:

1. Set Up Your Model - The RISKOptimizer Model window provides one-stop setup for all optimisation 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 maximised, minimised, or approach a specific goal.

2. Run the Optimisation - Click the Start icon to start the optimisation. 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 optimisation 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.

3. View Optimisation Results - After optimisation, 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 optimisation summary, log of all simulations, and log of progress steps.

Part of the DecisionTools Suite

RISKOptimizer is part of the DecisionTools Suite Industrial, Palisade's complete risk and decision analysis toolkit. The DecisionTools Suite adds PrecisionTree for decision trees, TopRank for what-if analysis, and more.

100% Excel

RISKOptimizer optimisation and 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? Correct results-and fast-using RISKOptimizer!

Compatibility: RISKOptimizer is compatible with Excel versions 2000 through 2007.

Read more about RISKOptimizer at www.palisade.com.