Joint Optimization of Age Replacement and Spare Ordering Policy Based on Genetic Algorithm

A joint strategy of stock provision and age-based preventive maintenance is considered. The joint strategy takes the form (T, s, S), where T is scheduled preventive replacement age, s is stock reorder point and S is maximum inventory level. The optimal values of the decision variables are obtained by minimizing the total cost of maintenance and inventory. To accomplish the optimizing task, a general approach of integrating discrete event simulation with genetic algorithm is proposed, where performance measures for specified strategy are obtained by simulation and optimal values are searched by genetic algorithm. The joint strategy is more realistic as it allows for many other assumptions, e.g. expected remaining life and retarded preventive replacement. Simulation method can easily adhere to the extended joint strategy. Numerical experiments are provided and the results clearly indicate that the optimization approach is more effective than that of the reference.