In this paper, a method for preventive maintenance scheduling optimization of standby systems, based on genetic algorithm and probabilistic safety analysis, is described. The goal of this approach is to improve the average availability of the system through the optimization of the preventive maintenance policy. Here, the genetic modeling propitiates unconstrained optimization, allowing nonconstant intervals between maintenance, adapting them to the aging parameter of the Weibull distribution used. In order to demonstrate the effectiveness of the proposed method, it is applied to a nuclear system of a two-loop pressurized water reactor. The results, when compared with those obtained by some standard maintenance policies, reveal gains at safety level.
