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

S-BORM: Reliability-based optimization of general systems using buffered optimization and reliability method