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Satellite services are fundamental to the global economy, and their design reflects a tradeoff between coverage and cost. Here, we report the discovery of two alternative 4-satellite constellations with 24- and 48-hour periods, both of which attain nearly continuous global coverage. The 4-satellite constellations harness energy from nonlinear orbital perturbation forces (e.g., Earth’s geopotential, gravitational effects of the sun and moon, and solar radiation pressure) to reduce their propellant and maintenance costs. Our findings demonstrate that small sacrifices in global coverage at user-specified longitudes allow operationally viable constellations with significantly reduced mass-to-orbit costs and increased design life. The 24-hour period constellation reduces the overall required vehicle mass budget for propellant by approximately 60% compared to a geostationary Earth orbit constellation with similar coverage over typical satellite lifetimes. Mass savings of this magnitude permit the use of less expensive launch vehicles, installation of additional instruments, and substantially improved mission life.

Real-world operational use of parallel multi-objective evolutionary algorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objective evolutionary algorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master-slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple masterslave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.

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