Bootstrap-based maximum multivariate CUSUM control chart

Khusna, Hidayatul; Mashuri, Muhammad; Ahsan, Muhammad; Suhartono, Suhartono; Prastyo, Dedy Dwi

doi:10.1080/16843703.2018.1535765

Cited by 34 publications

(15 citation statements)

References 41 publications

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“…The vectors of residual e i are assumed to have known in-control covariance matrix P eðgÞ . The MLS-SVR-based Max-MCUSUM statistics are developed from the Max-MCUSUM statistic for independent observation (Khusna et al, 2018). The statistics of MLS-SVR-based Max-MCUSUM are obtained by forming a random variable Z i as follows:…”

Section: The Proposed Control Chartmentioning

confidence: 99%

“…Cheng and Thaga (2005b) proposed Max-MCUSUM chart in which the reference value was calculated from actual observation, and its upper control limit (UCL) was estimated using the Markov Chain approach. Recently, Khusna, Mashuri, Ahsan, Suhartono, and Prastyo (2018) developed a bootstrap-based Max-MCUSUM control chart with two advantages. First, the researcher can adjust the level of tightness by predetermining the reference value in Phase I monitoring process.…”

Section: Introductionmentioning

confidence: 99%

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Residual-based maximum MCUSUM control chart for joint monitoring the mean and variability of multivariate autocorrelated processes

Khusna

Mashuri

Suhartono

et al. 2019

Production & Manufacturing Research

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Section: The Proposed Control Chartmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Residual-based maximum MCUSUM control chart for joint monitoring the mean and variability of multivariate autocorrelated processes

Khusna

Mashuri

Suhartono

et al. 2019

Production & Manufacturing Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…A close view of bootstrap control charts can be seen in the work of Jones and Woodall, 37 where extensive computer simulations were provided to assess the performance of bootstrap control charts in terms of the average run length (ARL). Since then, several bootstrap control charts have been developed with successful results, such as control charts for Weibull percentiles, 38 Birnbaum-Saunders percentiles, 39 inverse Gaussian percentiles, 40 and autocorrelated process data 41 and bootstrap-based maximum multivariate cumulative sum charts, 42 among others.…”

Section: Bootstrap Standard Deviation Control Chartmentioning

confidence: 99%

Control charts for traffic intensity monitoring of Markovian multiserver queues

Cruz

Quinino

2019

Quality & Reliability Eng

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A number of recent research studies have applied queueing theory as an approximate modeling tool to mathematically describe industrial systems, which include manufacturing, distribution, and service, for instance. Among the main observable characteristics in queues, the number of users in the system can be controlled to keep waiting times as minimal as possible. The design of efficient control charts is an attempt to monitor and control such systems. Control charts are proposed to monitor infinite queues with Markovian arrivals, exponential service times, and s identical parallel servers. The proposed charts monitor traffic intensities, which are the ratio between the arrival rate and the service rate, estimated through the number of users in the queueing system at random epochs. The effectiveness and efficiency of the proposed approaches in terms of the average run lengths are established by a comprehensive set of Monte Carlo simulations. KEYWORDS attribute control charts, average run length, Markovian queues, quality control wileyonlinelibrary.com/journal/qre [Colour figure can be viewed at wileyonlinelibrary.com] [Colour figure can be viewed at wileyonlinelibrary.com] Frederico R. B. Cruz holds a bachelor's degree in electrical engineering (1988) as well as a master's degree (1991) and a doctorate (1997) in computer science from the Universidade Federal de Minas Gerais, where he is a full professor in the Department of Statistics and conducts research in operations research.Roberto C. Quinino has a PhD in production engineering from the Universidade de São Paulo (1998). He is currently a full professor at the Universidade Federal de Minas Gerais. He develops research on quality control and marketing engineering with an emphasis on stochastic modeling applications acting on the following subjects: Taguchi online, Markov chains, misclassification, optimization, control chart planning, regression analysis, planning of experiments, and conjoint analysis. He supervises undergraduate, specialization, master's, and doctoral students. He has published papers in several international journals.

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“…Research works on MEWMA charts can be found in Lowry et al, Prabhu and Runger, Alkahtani and Schaffer, Park and Jun, Liang et al, Chiang et al, and Flury and Quaglino, to name a few. Meanwhile, readers can refer to previous studies for research works on the MCUSUM chart. The notations CMCUSUM and PRMCUSUM are used to denote the MCUSUM charts by Crosier and Pignatiello et al, respectively.…”

Section: Introductionmentioning

confidence: 99%

Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown

Haq

Khoo

2019

Quality & Reliability Eng

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Memory‐type multivariate charts have been widely recognized as a potentially powerful process monitoring tool because of their excellent speed in detecting small‐to‐moderate shifts in the mean vector of a multivariate normally distributed process, namely, the multivariate EWMA (MEWMA), double MEWMA, Crosier multivariate CUSUM (MCUSUM), and Pignatiello and Runger MCUSUM charts. These multivariate charts are based on the assumption that the covariance matrix is known in advance; but, it may not be known in practice. It is thus not possible to use these multivariate charts unless a large Phase I dataset is available from an in‐control process. In this paper, we propose multivariate charts with fixed and variable sampling intervals for the process mean vector when the covariance matrix is estimated from sample. Using the Monte Carlo simulation method, the run length characteristics of the multivariate charts are computed. It is shown that the in‐control and out‐of‐control run length performances of the proposed multivariate charts are robust to the changes in the process covariance matrix, while the existing multivariate charts are not. A real dataset is taken to explain the implementation of the proposed multivariate charts.

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Bootstrap-based maximum multivariate CUSUM control chart

Cited by 34 publications

References 41 publications

Residual-based maximum MCUSUM control chart for joint monitoring the mean and variability of multivariate autocorrelated processes

Residual-based maximum MCUSUM control chart for joint monitoring the mean and variability of multivariate autocorrelated processes

Control charts for traffic intensity monitoring of Markovian multiserver queues

Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown

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