New challenges and opportunities in reliability and risk based optimization

Safety (S) improvement of industrial installations leans on the optimal allocation of designs that use equipment that is more reliable and testing and maintenance activities to assure a high level of reliability, availability and maintainability (RAM) for their safety-related systems. However, this also requires assigning a certain amount of resources (C) that are usually limited. Therefore, the decision-maker in this context faces in general a multiple-objective optimization problem (MOP) based on RAMS+C criteria where the parameters of design, testing and maintenance act as decision variables. A general framework for such MOP based on RAMS+C criteria was proposed in [1]. There, a number of alternatives were proposed based on the use of a combination of RAMS+S formulation and Genetic Algorithms (GAs) based optimization to solve the problem of testing and maintenance optimization based only on system unavailablity and cost criteria. The results showed the capabilities and limitations of alternatives. Based on them, challenges were identified in this field and guidelines were provided for further research. In [2], a full scope application of RAMS+S based optimization using GAs was reported. Since then, the reliability and risk based optimization of design and operation of equipment and facilities has evolved into a set of technical documents, conference contributions and technical papers published elsewhere. Many of them have already addressed to some extent the effect of both random and epistemic uncertainties within this reliability and risk informed decision-making framework. This paper discusses the importance of appropriate formulation, treatment and analysis of model and parameter uncertainties in reliability and risk informed decision-making. It faces on how treatment and analysis of uncertainties should be integrated within an approach for evaluation of reliability and risk impact of safety issues, i.e. equipment design, operational requirements, etc. The approach would consist of modeling, assessment and analysis of the safety concern, which is intended to be used within an optimization context to support the decision-making on the most effective safety requirements. The paper focuses on Reliability and Risk of Nuclear Installations, where particular attention is paid to address the effect of uncertainties in the reliability and risk informed optimization of testing and maintenance of safety related equipment. Similar challenges can be observed for many other complex installations, such as energy generation and distributions, process industry, aeronautics, etc.