Joint optimization of production, maintenance, and quality control considering the product quality variance of a degraded system

Abstract

The joint optimization of production, maintenance, and quality control has shown effectiveness in reducing long-term operational costs in production systems. However, existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant. To address this limitation, we propose an innovative method based on the continuous ranking probability score (CRPS). This method enables the simultaneous detection of changes in mean and variance in nonconformities, thus removing the assumption of a specific distribution for quality characteristics. Our approach focuses on developing optimal strategies for production, maintenance, and quality control to minimize cost per unit of time. Additionally, we employ a stochastic model to optimize the production time allocated to the inventory buffer, resulting in significant cost reductions. The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments, sensitivity analysis, and a comparative study. The results show that our method can achieve cost reductions compared to several other related methods, highlighting its practical applicability for manufacturing companies aiming to reduce costs.

Graphical abstract

Keywords

joint optimization / degraded system / CRPS control chart / uncertain buffer stocking time

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