9 Pages Posted: 21 Oct 2008
This note describes the use of the OptQuest tool (updated July 2009) for optimal decision variable searches based on Crystal Ball Monte Carlo simulations. Setup of the objectives and constraints, selecting run options, saving results, and a simple example are all included.
Rev. July 23, 2009
Welcome to the new and improved world of OptQuest, a part of Crystal Ball (CB). This note is based on version 22.214.171.124.00 of CB, which introduces a much more user-friendly OptQuest. Maybe you'll find yourself having fun on weekends optimizing your decisions via Monte Carlo simulations!
The OptQuest tool will help you search for the optimal levels of many decision variables simultaneously using performance measures simulated by Monte Carlo simulations subject to constraints you specify. A decision table will suffice for a single decision variable with no constraints, but OptQuest will work for many decision quantities (discrete or continuous), even with nonlinear objectives and constraints. It can also accommodate requirements on secondary statistics, either a main performance measure or an alternative measure.
Because it considers the effects of uncertainty, OptQuest does more than Excel Solver. It can compute how many of several products to make, for example, in order to maximize profit without uncertainty and without running simulations. Alternatively, OptQuest can be set to find solutions that maximize the mean profit or minimize standard deviation of profit, or a number of other statistics based on CB simulations. This technical note will help a first-time user become comfortable using the software.
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Keywords: decision analysis, monte carlo simulation
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