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S-BORM: Reliability-based optimization of general systems using buffered optimization and reliability method
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DOI：10.1016/j.ress.2023.109314.png)
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Abstract
En 中文
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 various numerical examples with increasing complexity that include up to 10,062 cut-sets, which are solved by the proposed algorithm within a reasonable computational time with high accuracy. We also demonstrate the algorithm's robustness by performing extensive parametric studies.
Keywords:
Reliability-based optimization
Data-driven optimization
General systems
Buffered failure probability
Difference-of-convex programming
Proximal bundle method
Superquantile
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