| A recent tendency in designing Space Systems for a specific mission can be described easily and explicitly by the new design-to-cost philosophy, “faster, better, cheaper” (fast-track, innovative, lower-cost, small-sat). This means that Space Systems engineers must do more with less and in less time. This new philosophy can result in space exploration programs with smaller spacecraft, more frequent flights at a remarkably lower cost per flight (cost first, performance second), shorter development schedules, and more focused missions. Some early attempts at “faster, better, cheaper” possibly moved too fast and eliminated critical tests or did not “space-qualify” the innovations, causing failure. A new discipline of Constrained Optimization must be employed.; With this new philosophy, Space Systems Design becomes a difficult problem to model in the new, more challenging environment. The objective of Space Systems Design has moved from maximizing space mission performance under weak time and weak cost constraints (accepting schedule slippage and cost growth) but with technology risk constraints, to maximizing mission goals under firm cost and schedule constraints but with prudent technology risk constraints, or, equivalently maximizing “expected” space mission performance per unit cost. Within this mindset, a complex Conceptual Space Systems Design Model was formulated as a (simply bounded) Constrained Combinatorial Optimization Problem with Estimated Total Mission Cost (ETMC) as its objective function to be minimized and subsystems trade-offs and design parameters as the decision variables in its design space, using parametric estimating relationships (PERs) and cost estimating relationships (CERs). Here, given a complex Conceptual Space Systems Design Problem, a (simply bounded) Constrained Combinatorial Optimization “solution” is defined as the process of achieving the most favorable alternative for the system on the basis of objective decision-making evaluation criteria.; Two meta-heuristic optimization algorithms, Genetic Algorithms (GAs) and Simulated Annealing (SA), were used to optimize the formulated (simply bounded) Constrained Combinatorial Conceptual Space Systems Design Model. GAs and SA were demonstrated on the SAMPEX (Solar Anomalous & Magnetospheric Particle Explorer) Space System. The Conceptual Space Systems Design Model developed in this thesis can be used as an assessment tool to evaluate and validate Space System proposals. |
