It is widely recognized that there exists a close relationship between the health condition of manufacturing equipment and the overall quality of the manufactured product. It is, therefore, vital and of paramount practical importance and theoretical significance to develop optimized/integrated models of statistical process control (SPC) and maintenance planning (MP). The paper targets integration of the decisions of MP and SPC for a two-stage dependent manufacturing process. Each stage of the process can either be in the “in-control” state or in the “out-of-control” state such that transitions occur due to manufacturing equipment degradation/failure. Four control charts are developed to monitor the process by formulating the problem based on the renewal theory and considering different potential scenarios. Based on the intuitively appealing concept of opportunistic maintenance, we develop a novel integrated SPC and MP framework referred to as the Opportunistic Maintenance Integrated Model (OMIM), which takes into account both process and equipment conditions. A genetic algorithm (GA) is then applied to find the optimal values of the decision variables minimizing the long-run expected average cost per unit time. As a benchmark to evaluate the performance of the proposed OMIM, another integrated model referred to as the Non-Opportunistic Maintenance Integrated Model (NOMIM) is developed. Numerical results illustrate the superior performance of the proposed OMIM framework in comparison with its counterparts.
