  Reliability-based optimization (RBO) is crucial for identifying optimal
risk-informed decisions for designing and operating engineering systems.
However, its computation remains challenging as it requires a concurrent task
of optimization and reliability analysis. Moreover, computation becomes even
more complicated when considering performance of a general system, whose
failure event is represented as a link-set of cut-sets. This is because even
when component events have smooth and convex limit-state functions, the system
limit-state function has neither property, except in trivial cases. To address
the challenge, this study develops an efficient algorithm to solve RBO problems
of general system events. We employ the buffered optimization and reliability
method (BORM), which utilizes, instead of the conventional failure probability
definition, the buffered failure probability. The proposed algorithm solves a
sequence of difference-of-convex RBO models iteratively by employing a proximal
bundle method. For demonstration, we design three numerical examples with
increasing complexity that includes up to 108 cut-sets, which are solved by the
proposed algorithm within a minute with high accuracy. We also demonstrate its
robustness by performing extensive parametric studies.
