Review and comparison of system reliability optimization algorithms considering reliability estimation uncertainty

System reliability optimization involves the selection of components and a system architecture to maximize reliability. Most existing studies assume that the reliability values of the components are deterministic, and known with certainty. However, in practice the reliability of the component is just a point estimate, and therefore, there is some uncertainties associated with it. In this paper, we review and critique the available different methodologies for system reliability optimization which explicitly considers uncertainty. One approach is defining a lower percentile of system reliability distribution and using this lower-bound limit as the objective function to be maximized. Another approach is formulating the problem as a multi-objective optimization problem, i.e., maximizing the reliability estimate and minimizing the associated variance. Finally, we will review a new approach where the objective function is to minimize the coefficient of variation of the system reliability estimate with respect to a minimum system reliability constraint and other system level constraints.