Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Decision-Analytic Approach to Reliability-Based Design Optimization
Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective reliability-based optimization for design of a vehicledoor
Finite Elements in Analysis and Design
Sampling-based approach for design optimization in the presence of interval variables
Structural and Multidisciplinary Optimization
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The problem of aircraft sizing during conceptual design is characterized by limited knowledge and high uncertainty. Uncertainty is especially prevalent in the early-phase estimates of design characteristics from the aerodynamics, propulsion, and weights discipline areas. In order to develop effective conceptual designs that are robust and fare well in later program phases, trade space exploration and optimization should favor design choices that are both "balanced" in terms of the multiple performance objectives and resistant to system-level losses due to missed targets for disciplinary metrics. This paper presents a study of the effects of uncertainty in multi-objective optimization in aircraft conceptual design by demonstrating the changes in the Pareto frontiers due to variability in disciplinary metrics and differences in the formulation of the probabilistic optimization problem. By analyzing these frontiers, the decision maker can judge the tradeoff between expected performance and resistance to uncertainty and can identify regions of the design space where this tradeoff is either favorable or high risk, resulting in improved decision making. To enable this analysis, multi-objective optimization and visualization techniques are tailored to the problem by incorporating Monte Carlo methods and other mechanisms of quantitatively capturing the effects of uncertainty.