Managing variability in modern production systems presents a critical challenge where traditional static maintenance and buffer management policies are often suboptimal. This paper introduces and evaluates an integrated and adaptive control strategy that dynamically combines preventive maintenance (PM) and buffer allocation. Unlike fixed scheduling, the PM policy triggers interventions based on buffer level thresholds and elapsed time, while buffer capacities adapt in real time according to machine blocking percentages. The strategy’s effectiveness was tested via a simulation model of a flow line, using a factorial design to analyze performance across 16 different scenarios. The results show that the fully integrated model provides substantial improvements, with an average reduction in machine processing time of 16.5% and a decrease in unit cost exceeding 50% in optimal configurations compared to the fixed case. Analysis of Variance (ANOVA) also identified buffer capacity and PM frequency as the most influential control parameters. In contrast, configurations optimizing only buffers or only PM yielded modest gains. These findings highlight the powerful synergistic impact of coupling buffer flexibility with adaptive maintenance, demonstrating that responsive, data-driven policies are fundamental to enhancing efficiency and economic competitiveness in Industry 4.0 production systems.
Paolo Renna (Thu,) studied this question.