These studies tested the effectiveness of simple preventive maintenance policies using discrete-event simulation, rather than optimizing them along with production scheduling decisions. There are also other research that extend the simple machine scheduling models by considering the maintenance decisions or constraints (Mannur and Addagatla, 1993). A multi- criteria approach to find optimal preventive maintenance intervals of components in a paper factory production line with total expected costs and reliability as the objective functions was proposed by Chareonsuk et al. (1997). Gharbi and Kenne´ (2005) could find an approximation for optimal control policies and values of input factors by combining analytical formulation with simulation-based statistical tools such as experimental design and response surface methodology in a production and preventive maintenance planning problem. A comprehensive research in the area of integrating preventive maintenance scheduling and pro- duction planning was found in Ruiz et al. (2007). In this study, three different policies for preventive maintenance schedules and the total manufacturing timewere defined for flowshop problems. The authors applied six different adaptations of heuristic and metaheuristic algorithms to evaluate the policies over two sets of problems and concluded that ant colony optimization and genetic algorithms solve these types of problems effectively overcoming other types of metaheuristics.