Robust design of ARMA and ACC charts for imperfect and autocorrelated processes under uncertainty

Abstract

Occurring disruptions and faults violate the perfectness of a production process in determining Economic Production Quantity (EPQ). Integrating EPQ, Statistical Process Control (SPC), and maintenance terms has brought satisfactory outcomes for such an imperfect process. Under the violated independence assumption, the Autoregressive Moving Average (ARMA) chart has been applied in the integrated models. Under autocorrelation, the extended Acceptance control chart (ACC) has indicated significant reductions in costs. Nevertheless, it has not been applied to any integrated modeling yet. Until now, constant input factors have been considered for modeling imperfect and autocorrelated processes. Uncertainty in estimating those factors can deteriorate the effectiveness of models. We present the first robust design for imperfect and autocorrelated processes under uncertainty. We apply a particle swarm optimization algorithm to provide solutions. The investigations of simple and integrated modeling indicate that applying the ACCs causes better statistical and economic results. Also, a real-life application is presented.

KEYWORDS:

• Acceptance control chart
• ARMA chart
• autocorrelation
• robust design
• particle swarm optimization

Data availability statement

The data and programming codes that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).