Genetic algorithms in optimizing surveillance and maintenance of components

Nowadays the great ability of genetic algorithms (GA) to find solutions in complex optimization problems is known, where other methods used to give poor results. This has opened a wide range of application areas using GA and here we present a new approach aimed at the global and constrained optimization of surveillance and maintenance (S&M) of components based on risk and cost criteria. Also, a case study is performed using this approach which shows the benefits of the integration of S&M tasks of components based on optimized intervals. Moreover, this methodology is completely valid in solving other optimization problems with respect to risk and cost beyond the component level.