A MATHEMATICAL MODEL FOR DYNAMIC MAINTENANCE GROUPING CONSIDERING DISCRETE PERFORMANCE OF A MULTI-COMPONENT SYSTEM

The aim of this paper is to present a method to optimize maintenance planning for a exible manufacturing system. Such a system can be considered as a multicomponent system. Two types of methods may be used in the maintenance optimization of multi-component systems, i.e., static or dynamic methods. Static methods provide a xed maintenance planning, whereas dynamic methods redene the groups of maintenance operations at each decision time. Dynamic or opportunistic maintenance can incorporate up to date information such as 1) machines condition, 2) the number of maintenance teams, and 3) production-related constraints in redening the groups of maintenance operations. As the literature review shows, the existing dynamic or opportunistic maintenance models are mainly developed to specify classes of multi-component systems that are expected to operate continuously without considering the production-related constraints and performance indicators. The objective of this paper is to develop the existing dynamic opportunistic maintenance approaches for exible production systems that operate intermittently. To this end, a mixed-integer nonlinear mathematical model is developed to simultaneously decide on the maintenance grouping as well as lot sizing and production schedule. Moreover, the proposed model considers further underlying assumptions such as 1) the limited number of maintenance teams, 2) initial inventory, 3) assembly operations, 4) lot sizing, 5) sequence-dependent setup times, 6) safety stock levels, and 7) lots with unequal and variable sizes. The objective includes the costs of preventive and corrective repair activities as well as various production costs consisting of production and setup costs, tardiness penalty costs, and safety stock penalty costs. Due to the nonlinear nature of the failure rate of the production machines, techniques for solving linear mathematical models cannot be used. From this, a linear approximation of the model is presented. The validity and eciency of the proposed model were analyzed by implementation in a numerical experiment.