عنوان مقاله [English]
Both in the academic environment and in the realm of operation, it has been shown that there is a close relationship between product quality and maintenance of equipment. Thus, coordination of the decisions associated with maintenance and quality can lead to decrease in operational costs of a production system and improve the overall productivity. To coordinate the decisions related to maintenance and statistical process control, this thesis develops integrated mathematical models. The system performance is compared in two states: (1) while there is a coordination between the decisions of maintenance and SPC, (2) while the decisions of maintenance and SPC are conducted separately, and indeed, there is no coordination between SPC and maintenance. To determine the system performance in state 1, an integrated model of SPC and maintenance is developed. Optimization of the integrated model determines the value of ECT in state 1. To determine the system performance in the uncoordinated state,i.e., state 2, the following steps are performed: (1) a stand-alone model of maintenance is proposed, (2) a stand-alone model of SPC is developed, (3) according to the results of optimization of step 1 and 2, the value of ECT in the uncoordinated state is computed. Finally, comparison of the values of ECT in the coordinated state and uncoordinated state clarifies that, due to the performance of the integrated model, how much saving in operational cost is obtained. For the systems investigated in the thesis, using factorial designs, the effect of the system parameters on the decision variables and the objective function is analyzed. Also, it is investigated that, with respect to the operational costs, what circumstances are more suitable for implementing the integrated models of SPC and maintenance. According to the results of the study, integration of the decisions associated with maintenance and SPC leads to a better performance. The amount of saving obtained from the coordination is significant for the different production systems.