Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system

•The integrated model considers the three key functions for economic success.•Production, preventive maintenance and quality control rates are jointly optimized.•A dynamic sampling strategy is proposed to face the effects of deterioration.•The quality sampling plan ensures a quality constraint required by customers.•The proposed policy leads to considerable cost savings compared to other policies. This paper presents a control policy of integrated production, maintenance and quality control planning for a continuous production system subject to quality deterioration. The system under study is composed of an unreliable machine that produces one part type satisfying customers demand. The machine can fail at any time and is subject to quality deterioration, and so a preventive maintenance and quality control policies are proposed to decrease the rate of defectives and to increase the system availability. The proposed production policy incorporates production thresholds, which regulate the machine production rate. Traditional sampling inspections standards such as ANSI/ASQC Z1.4 and ISO 2859 have addressed a dynamic quality control level to face quality deterioration. However, these standards are based only on quality considerations, disregarding the economic aspects and the interactions with production and maintenance management in the design of sampling plans. Thus, the proposed integrated model analyses in detail the effect of such dynamic sampling strategy and relevant interactions with production and maintenance strategies. The main objective of the paper is to determine an appropriate production policy as well as the preventive maintenance and the quality control rates in order to minimize the expected average incurred cost and satisfy at the same time a quality constraint. Given the high flexibility and capacity to model complex manufacturing systems, a combination of simulation modeling and optimization techniques are used to determine a solution for this stochastic and constrained problem. In addition, numerical examples and an extensive sensitivity analysis are conducted to illustrate the proposed control approach. Furthermore, we compared the proposed integrated policy with three common policies from the literature. Such study serve us to highlight the effectiveness of the approach, since the proposed integrated policy led to considerable cost savings.