Genetic algorithms in optimizing surveillance and maintenance of components

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

“…Munoz et al [86] use the GA at the global and constrained optimization of surveillance and maintenance of components based on risk and cost criteria. Contrary to the common application, they use decimal representation in their chromosomes instead of a binary representation.…”

Optimizing the preventive maintenance scheduling by genetic algorithm based on cost and reliability in National Iranian Drilling Company

“…In this respect, some researchers also focus on the optimization of preventive maintenance scheduling (Moradi et al 2011;Xiaojun et al 2012). Munoz et al (1997) are among the first researchers who proposed the genetic algorithm as an optimization tool for preventive maintenance scheduling (Lapa et al 1999(Lapa et al , 2000Munoz et al 1997) and then Lapa et al (2006) used the genetic algorithm for the optimization of maintenance and inspection intervals in a new approach. Most of the schedules were originally developed for power plants but shortly after that the optimized scheduling was employed for mechanical components (Tsai et al 2001) and then for production lines (Sortrakul et al 2005).…”

Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

“…Several authors [1][2][3][4] have emphasized the potential of risk-informed approach and its application to nuclear as well as non-nuclear/chemical industries also. The specific activities related for their resource effectiveness in riskinformed applications are evaluation of technical specifications [1], in-service inspection [2], preventive maintenance, and in-service test.…”

“…Resolution of such complex optimization problems requires numerical methods. However, as traditional approaches usually give poor results under these circumstances, new methods based on genetic algorithms (GAs) were investigated in order to try to solve this kind of complex optimization problems [1][2][3][4]. Martorell [1] and Vaurio [2] have successfully applied to TI optimization problems.…”

“…However, the parameters such as failure rate, demand failure probability and repair time are considered to be constants in earlier work done on applications of riskinformed decision making [1][2][3][4]. Nevertheless, uncertainty in the failure/repair parameters of the components is inevitable in any kind of reliability calculations.…”