The Proactive Base Stock Policy for Condition Based Maintenance

Abstract

This paper introduces the proactive base stock policy (ProBSP) that incorporates degradation data into spare parts decision-making processes. The proposed policy aims to anticipate the demand for spare parts by proactively ordering them, instead of ordering each time one is needed. ProBSP involves two decision variables: the initial stock level and the degradation order threshold. To determine the optimal values for these variables, a simulation-based optimization method is developed. More precisely, a discrete event simulation is constructed to evaluate a single point in the parameter space, and an intelligent algorithm is employed to search for the optimal solution by efficiently exploring the solution space, thereby exploiting structural properties of ProBSP. Finally, numerical experiments are conducted to gain insights into the performance of ProBSP. These experiments reveal the effectiveness of ProBSP: the average spare parts stock level is reduced by 68%, with reductions exceeding 90% in specific scenarios.