Robust Modeling of Acceptance Control Chart to Specify Best Design Parameters

Jafarian‐Namin, Samrad; Nezhad, Mohammad Saber Fallah; Tavakkoli–Moghaddam, Reza; Mirzabaghi, Mehrdad

doi:10.1007/978-3-030-47124-8_26

Cited by 1 publication

(1 citation statement)

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“…Designing attribute control charts with different sampling schemes, similar to [59], can be extended under the integrated model. Uncertainty in the model can be treated using a robust optimization approach applied in [54] and [60]. Recently, the ARMA control chart was applied to a ten-scenario model [61].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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 autoregressive 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.

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Section: Discussionmentioning

confidence: 99%