## 1 Introduction into the Problem Area
Finally, they conduct computational experiments which demonstrate the effectiveness and validity of the proposed hybrid metaheuristic. The active preventive maintenance approach for complex equipment is proposed in [6]. Firstly, a novel PSS operation model was developed, where complex equipment is offered as a part of PSS (product-service system) and under exclusive control by the providers. Then, a solution of equipment preventive maintenance based on the operation mode was designed. A deep neural network was trained to predict the remaining effective life of the key components, and thereby, it can pre-emptively assess the health status of equipment. When implementing the TPM concept for optimising maintenance processes, it was found that difficulties are encountered not so much in the introduction as in the permanent application of TPM. In particular, the production staff have to be convinced of the usefulness of TPM. TPM was successfully established with the aid of audits and staff training, which was proven by the positive development of the audit marks and by staff-opinion polls. Within five years TPM was successfully implemented by private and national owners at about 50 plants predominantly in Japan, China and USA, resulting in a continuous improvement of their availability [7]. The preventive maintenance scheduling problem is studied in [8] on behalf of generation companies (GENCOs) with natural gas power plants, while taking into account their signed natural gas contracts and the opportunities of purchasing and selling natural gas in the spot market. They consider the uncertain prices of both natural gas and electricity in the spot market, and propose a multistage stochastic mixed integer programming (MSMIP) model seeking the optimal operations regarding maintenance outage scheduling and natural gas trading. Large-scale MSMIP problems suffer not only the curse of dimensionality, but also computational difficulties with both discrete and continuous variables at each stage. [PERSON] et.al. [9] leverage the progressive hedging algorithm based on scenario-based decomposition to solve large MSMIP problems. The solutions obtained from the algorithm exhibit promising quality under their numerical studies. In order to improve the cost-efficiency of the serial production lines, a deep reinforcement learning-based approach is proposed to obtain PM policy. A novel modelling method for the serial production line is adopted during the learning process. A reward function is proposed based on system production loss evaluation. The algorithm based on the Double Deep Q-Network is applied to learn the PM policy. The TPM methodology is widely used in practice as evidenced by the following publications [10; 11; 12; 13; 14; 15; 16; 17; 18]. Each of the above works operate with the classic TPM methodology, but our approach has adjusted the individual parameters of the methodology and completely new ones have been delivered, thus creating a unique methodology that can be used in industry. In the TPM methodology, preventive maintenance (Figure 3) is located at the top of the maintenance management right after the most complex method regarding implementation, namely predictive maintenance, which tries to predict future conditions.