A decision support process using many‐objective optimization, high‐performance computing, and advanced visualization is applied to gain comprehensive insight into multistakeholder portfolio budgeting trades. The key tradeoffs among nondominated portfolio budget solutions are systematically identified and examined with respect to different stakeholder viewpoints. The approach is illustrated using a portfolio of 14 U.S. Air Force satellite development programs using budget data taken from the 2010 Future Year Development Plan [http://www.saffm.hq.af.mil/budget/, last accessed April 20, 2011]. Practical lessons learned in applying the approach are discussed. ©2012 Wiley Periodicals, Inc. Syst Eng 15

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We investigate the ability of a reduction in the complexity of a model to enable a more rapid optimization of that model, with minimal reduction in accuracy, if critical parameters and features are identified and contained within the reduced model. We present a Model Diagnostics analysis of the Draim global coverage constellations and the underlying coverage model used to analyze their performance using Sobol's method of sensitivity indices. We propose complexity reduction based on the diagnostic analysis. Two separate optimizations are run using a Multi-Objective Evolutionary Algorithm optimization tool. One optimization uses the original problem formulation while the other uses a reduced-complexity formulation. We demonstrate that subtle behavior in the interaction of all parameters (not only the critical ones) dramatically affects performance of the optimization. Nomenclature= First Order Sensitivity index of parameter i = Second Order Sensitivity index of parameter i = Total Order Sensitivity index of parameter i = Interactive Sensitivity index of parameter i = Semi-major Axis for satellite = Eccentricity for satellite = Inclination for satellite = Right Ascension of the Ascending Node (RAAN) for satellite = Argument of Perigee for satellite = Mean Anomaly for satellite = Minimum Elevation Angle