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.
Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem / Pisacane, Ornella; Potena, Domenico; Antomarioni, Sara; Bevilacqua, Maurizio; Emanuele Ciarapica, Filippo; Diamantini, Claudia. - In: ENGINEERING OPTIMIZATION. - ISSN 0305-215X. - 53:10(2021), pp. 1752-1771. [10.1080/0305215X.2020.1823381]
Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem
Pisacane, Ornella;Potena, Domenico;Antomarioni, Sara;Bevilacqua, Maurizio;Emanuele Ciarapica, Filippo;Diamantini, Claudia
2021-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.