Abstract
The close relationship between statistical process control and maintenance has attracted lots of researchers to focus on the jointly economic design of control chart (a main tool of statistical process control) and preventive maintenance policy, and much progress has been made in this field. However, in the existing literatures, the X- chart is used most, and other charts are rarely considered. In this paper, the economic design of CUSUM chart and age-based imperfect preventive maintenance policy is presented. The process is considered as a multiphase system, and a recursive algorithm is used to model each phase. Besides, a sampling policy under the non-Markovian deterioration assumption is employed, and an age-based imperfect preventive maintenance policy is used. An optimization model with the objective of minimizing the expected cost per unit time is constructed to obtain the near-optimal solution of decision variables: the age of the machine for maintenance, the number of age-based maintenances, sample size, sampling intervals, and the decision interval coefficient and reference value coefficient of CUSUM chart. The solution procedure of the model is provided. Also, sensitivity analysis is performed on the decision variables for each of the various parameters.
Highlights
The close relationship between quality and maintenance has led lots of researchers to focus on the integrated model of control chart and maintenance which are more realistic in practice
We have developed an integrated model to simultaneously optimize the parameters of CUSUM control chart and agebased preventive maintenance (PM) policy, in which, a more complex multiphase process of multiple PMs and multiple samplings in each PM interval is considered
The imperfect PM policy is used in the model in which each PM restores the process to a state between as good as new and as bad as old
Summary
Since Duncan [1] first proposed an economic design method of X charts to maintain current control of a process; the economic design of control charts has been one import issue in the quality control field. Optimizing maintenance strategy is a hot issue in the reliability field. In the course of statistical process control, planned/preventive maintenances need to be carried out to decrease the failure rate of the machine and reduce product variation. Corrective maintenances need to be performed to restore an out-of-control state back to an in-control state and have an impact on the failure mode of the machine which leads to a reduction in quality shift [2, 3] and further change the process control requirement. The close relationship between quality and maintenance has led lots of researchers to focus on the integrated model of control chart and maintenance which are more realistic in practice
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