Joint optimization of spare parts inventory and maintenance policies using hybrid genetic algorithms

In general, the maintenance and spare parts inventory policies are treated either separately or sequentially in industry. Since the stock level of spare parts is often dependent on the maintenance policies, it is a better practice to deal with these problems simultaneously. In this study, a simulation optimization approach using hybrid genetic algorithms (HGA) has been proposed for the joint optimization of preventive maintenance and spare provisioning policies of a manufacturing system operating in automotive sector. The HGA is formed using the probabilistic acceptance rule of the Simulated Annealing (SA) within the Genetic Algorithm (GA) framework. The cost function is evaluated by integrating the GA with a simulation model of the motor block manufacturing line, which represents the manufacturing system behaviour with its maintenance, and inventory related aspects. Next, to further improve the performance of the GA developed, a set of experiments has been performed to identify appropriate values for the GA parameters (i.e. the size of the population, the crossover probability, and the mutation probability). Finally, various comparative experiments have been carried out to evaluate performance of both the pure GA and HGA.