Maintenance Optimization for a Production System Subject to Shocks Considering a Buffer Inventory and Production Defects

In this paper, an innovative maintenance strategy is proposed for a system operating exposed to shock environments. The system contains a deteriorating production machine and a buffer inventory. As the number of completed production batches increases, the machine and the associated process state deteriorate. Accordingly, the production defect rate increases, perhaps to an unsatisfactory level. Two types of shocks, both arriving stochastically, also increase the defect rate or cause failures. To restore or improve the machine performance at decision times, one action is determined among four selectable options. Considering the complexity of the problem, a hybrid method is proposed to optimize the maintenance strategy. The hybrid method involves the integration of a modified human decision-making method (MHDM), Monte Carlo simulation and genetic algorithm (GA). MHDM is used to select effective maintenance activities at the decision times by considering relevant system information, e.g., buffer inventory levels, production defects, maintenance effects and previous decisions. Then, based on the established mathematical model, Monte Carlo simulation and GA are combined to adjust the key parameters of MHDM, to optimize the policy and minimize the expected cost rate. Finally, numerical examples are given to illustrate the proposed method, test its accuracy and provide sensitivity analyses.