There are numerous models in the literature for the maintenance of mechanical components. Most of them believe that the effect of maintenance work is “like new”. Whether the action suffered is preventive or corrective, it is assumed that it is equivalent to its replacement. This assumption is questionable in many cases. Maintenance without any replacement leads to a significant level of “greening” of a system, both before and after a repair action. In most cases performance recovery is incomplete. Every industrial installation is subject to aging. The actual effect of wear depends on the system under consideration, but in any case aging reduces the overall performance of the system, e.g. increased failure rate, increased energy consumption, fouling due to nominal operating conditions or reduced productivity of the system. . The situation is very different in the case of maintenance, for example, if the objective is to extend the intrinsic life of a very expensive piece of equipment to be replaced systematically. The objective of preventive interventions is to reduce the effect of system wear or delay the onset of these effects. Deterministic optimization models have been proposed by various authors. Yao et al (2001) presented a two-level hierarchical structure model that optimizes preventive maintenance planning for operations in the semiconductor manufacturing industry. For the upper level, a planning model based on the Markov decision-making framework captures both the dynamic failure process of the cluster tools and the production planning dynamics and is used to derive optimal policies for the long-term horizon. At the lower level, an efficient mixed integer programming model, which is located in the middle of the sheet of paper and the failure rate immediately preceding failure. Their algorithm determines the optimal schedule of maintenance actions before each replacement action in order to minimize the total cost over a placement horizon. We can find that most studies focus on single-component systems or simple and specific systems, which is not always applicable for real and general systems. Furthermore, not much work has been done in the field of age reduction models and enhancement factors. Kamran S.Moghaddam and John S.Usher proposed preventive maintenance and replacement planning models that address multicomponent systems and can be applied to a wide variety of systems. Since they use the concept of age reduction and improvement factor in these models, they also developed mathematical and statistical models to estimate the improvement factor for imperfect maintenance activities.