A maintenance scheduling optimization model for a multi-component machine in a digitalized manufacturing context

Abstract

Abstract Manufacturing systems are facing a digital transformation that supports data-driven decision making and the improvement of the efficiency of condition-based or predictive maintenance systems. In this context, international standards and previous works related to the architecture of condition-based maintenance (CBM) systems are available, and the interest in maintenance scheduling optimization, triggered by failure predictions, are increasing. This work is framed within the scope of the PICOePRO project and considers a critical multi-component welding machine. This paper first provides a predictive maintenance architecture, coherent with the OS A-CBM standard and proposed within the project, and then develops one of the modules of this system, in which the main component is a maintenance scheduling optimization model. The defined architecture allows real-time decision-making, and the proposed model minimizes the machine maintenance cost, exploring the potential for cost-saving by grouping maintenance and using machine downtime to perform maintenance activities. The functioning of the module and the potential gains from the adoption of the developed model are illustrated using a numerical example. The architecture and the model provide a viable approach to guide maintenance decision-making in the era of digitalized manufacturing.