An integrated model for optimal selection of quality, maintenance, and production parameters with autocorrelated data

Document Type : Article

Authors

1 Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, P.O. BOX 89195-741, Iran.

2 Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, P.O. BOX 89195-741, Iran

3 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

4 Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Qom, Iran

Abstract

Statistical process monitoring, maintenance policy, and production have commonly been studied separately in the literature, whereas their integration can lead to more favorable conditions for the entire production system. Among all studies on integrated models, the underlying process is assumed to generate independent data. However, there are practical examples in which this assumption is violated because of the extraction of correlation patterns. Autocorrelation causes numerous false alarms when the process is in the in-control state or makes the traditional control charts react slowly to the detection of an out-of-control state. The auto-regressive moving average (ARMA) control chart is selected as an effective tool for monitoring autocorrelated data. Therefore, an integrated model subject to some constraints is proposed to determine the optimal decision variables of the ARMA control chart, economic production quantity, and maintenance policy in the presence of autocorrelated data. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to search for optimal decision variables. An industrial example and some comparisons are provided for more investigations. Moreover, sensitivity analysis is carried out to study the effects of model parameters on the solution of the economic-statistical design.

Keywords

References

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Scientia Iranica
Volume 31, Issue 3 - Serial Number 3
Transactions on Industrial Engineering (E)
January and February 2024
Pages 206-227

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