A multi-objective genetic algorithm for RAMS+C optimization with uncertain decision variables

Abstract

Surveillance requirements applied to safety systems of a nuclear power plant are established by the technical specification (TS). These requirements impose the surveillance test intervals (STIs) performed to ensure that safety-related systems normally in standby are ready to operate on demand. There is a great interest in the optimization of STIs, as they have a great effect on plant risk and also on the resources necessary to implement a certain surveillance test strategy. Operational experience at nuclear power plants shows that tests are not performed at a constant interval. Thus, it is a more realistic choice to consider the STI as a random variable rather than a constant value, as it has usually been considered. This paper focuses on the STI optimization based on risk and cost criteria and considering that the test intervals can be performed within a tolerance range.