Using Generalized Stochastic Petri Nets for Preventive Maintenance Optimization in Automated Manufacturing Systems

This paper presents periodically preventive maintenance (PM) procedures for an automated manufacturing system (AMS) with series-parallel characteristics. The reliability of machines that comprise of above AMS will be influenced by each other. A system like this may develop latent failures and its reliability may vary, depending on scheduled checks and maintenance. Accordingly, we have modeled the deteriorated behavior of machines of AMS, and the effect of their reliability with PM activities, by using generalized stochastic Petri nets (GSPN) in order to measure the variation in reliability of those machines. In this study, it is assumed that different levels of PM will lead to different degrees of functional recovery. Different PM are considered simultaneously in order to determine the PM schedule of a given system. Consequently, it can determine an optimal combination of each PM stage with the criterion that the total cost of system maintenance must be minimized. In this paper, we present a realistic example of annual maintenance scheduling for an AMS to demonstrate the effectiveness of our proposed methodology. Furthermore, Taguchi method is used to validate the correctness of the application of GSPN on determination of PM for a complicated system.