In systems with many components that are required to be constantly active, such as refineries, predicting the components that will break in a time interval after a stoppage may significantly increase their reliability. However, predicting the set of components to be repaired is a challenging task, especially when several conditions (e.g. breakage probability, repair time and cost) have to be considered simultaneously. A data-driven predictive maintenance policy is proposed for maximizing the system reliability and minimizing the maximum repair time, considering both budget and human resources constraints. Therefore, a data-driven algorithm is designed for extracting component breakage probabilities. Then, two bi-objective optimization approaches are proposed for determining the set of components to repair. The former is based on the formulation of a bi-objective mixed integer linear programming model solved through the AUGMEnted ε-CONstraint (AUGMECON) method. The latter implements a bi-objective large neighbourhood search, outperforming the first approach.

在需要持续运行的许多组件的系统中，例如炼油厂，预测在停工后的时间间隔内会损坏的组件可能会显着提高其可靠性。然而，预测要修复的组件集是一项具有挑战性的任务，尤其是在多种情况下（例如损坏概率、维修时间和成本）必须同时考虑。考虑到预算和人力资源限制，提出了一种数据驱动的预测性维护策略，以最大限度地提高系统可靠性并最大限度地减少最长维修时间。因此，设计了一种数据驱动算法来提取组件破损概率。然后，提出了两种双目标优化方法来确定要修复的组件集。前者基于通过AUGMented ε -CONstraint (AUGMECON) 方法求解的双目标混合整数线性规划模型的公式化。后者实现了双目标大邻域搜索，优于第一种方法。