عنوان مقاله [English]
One of the most important goals of organizations and manufacturing companies are to provide suitable products and services to customers, which requires high quality processes and keeping it at the desired quality level. In many cases, product quality is low due to equipment deterioration, but it cannot be figured out until the equipment break-down. On the other hand control charts can be used to identify the condition of the process, where out-of-control state for a quality characteristic means deterioration in the equipment which is used for manufacturing purpose. Hence, statistical process control and maintenance decisions can be combined to form an integrated model which has more efficiency in reducing costs of quality and maintenance. Furthermore, using delayed monitoring policy to monitor processes is one of the newest research fields in this regard. Delayed monitoring means that processes are in control at the beginning of the process and sampling can be delayed until the pre-specified scheduled time. With delayed monitoring policy, the total cost of production per unit is expected to be more affordable as the sampling rate decreases but it may be lead to increase in the quality and maintenance costs. So, determining efficient decision variables are important in the model.
In this paper, an integrated statistical process control and maintenance model based on delayed monitoring is designed for a two-stage process. By using this procedure, 28 different scenarios are created in which variation in quality and different break-down states are considered. X ̅/residual mean control charts have been used for monitoring purpose. In order to integrate statistical process control as well as maintenance, a model is proposed such that the expected cost per time unit of manufacturing is minimized by using genetic algorithm. To evaluate the performance of the proposed method, an illustrative example is presented. The results show the appropriate performance of the proposed model. In addition, sensitivity analyses on some parameters of the proposed model are done.