Multiobjective spare part allocation by means of genetic algorithms and Monte Carlo simulation

The management of spare parts is a major concern for several industrial organizations, due to the significant amount of resources invested every year for holding spares inventories. s paper, we explore the possibility of using genetic algorithms for the task of optimizing the number of spare parts required by a multi-component system. To address the question of how many spares should be kept in inventory for each component kind, the analyst is required to define objective functions with respect to which the optimization is sought. In our work we will look at multiple objectives such as, for example, the maximization of system revenues and the minimization of the total spares volume. The modeling of the system failure, repair and replacement stochastic processes is done by means of Monte Carlo simulation, whose flexibility allows a closer adherence to reality.