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A control chart using belief information for a gamma distribution

100%
ASLAM M., KHAN N., JUN C.-H.
Operations Research and Decisions
|
2016
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vol. 26
|
issue 4
5-19
EN
The design of a control chart has been presented using a belief estimator by assuming that the quantitative characteristic of interest follows the gamma distribution. The authors present the structure of the proposed chart and derive the average run lengths for in-control and a shifted process. The average run lengths for various specified parameters have been reported. The efficiency of the proposed chart has been compared to existing control charts. The application of the proposed chart is illustrated with the help of simulated data.
2
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Moving Average control charts for Burr X and Inverse Gaussian distributions

100%
SHAFQAT A., ASLAM M., ALBASSAM M.
Operations Research and Decisions
|
2020
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vol. 30
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issue 4
81-94
EN
The Burr X and inverse Gaussian (IG) distributions have been considered to design an attribute control chart for time truncated life test with the moving average (MA) scheme w. The presentation of the MA control chart has been estimated in terms of average run length (ARL) by using the Monte Carlo simulation. The ARL is determined for different values of sample sizes, MA statistics size, parameters’ values, and specified average run length. The performance of this new MA attribute control chart has been compared with the usual time truncated control chart for Burr X and IG distributions. The performance of a new control chart is better than that of the existing control chart.
3
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An EWMA control chart for the exponential distribution using repetitive sampling plan

88%
AZAM M., ASLAM M., JUN C.-H.
Operations Research and Decisions
|
2017
|
vol. 27
|
issue 2
5-19
EN
A new EWMA control chart has been proposed under repetitive sampling when a quantitative characteristic follows the exponential distribution. The properties of the proposed chart, including the average run lengths has been is compared with two existing control charts with the help of simulated data. An application of the proposed chart hs been illustrated using a healthcare data set.
4
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A mixed control chart adapted to the truncated life test based on the Weibull distribution

88%
KHAN N., ASLAM M., KIM K.-J., JUN C.-H.
Operations Research and Decisions
|
2017
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vol. 27
|
issue 1
43-55
EN
The design of a new mixed attribute control chart adapted to a truncated life test has been pre-sented. It was assumed that the lifetime of a product follows the Weibull distribution and the number of failures was observed using a truncated life test, where the test duration was specified as a fraction of the mean lifespan. The proposed control chart consists of two pairs of control limits based on a bi-nomial distribution and one lower bound. The average run length of the chart was determined for vari-ous levels of shift constants and specified parameters. The efficiency of the chart is compared with an existing control chart in terms of the average run length. The application of the proposed chart is dis-cussed with the aid of a simulation study.
5
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Statystyczne sterowanie procesami o danych stochastycznie zależnych — pułapki rozwiązań standardowych

75%
Hryniewicz O.
Folia Oeconomica Cracoviensia
|
2013
|
vol. 54
90-106
EN
Shewhart control charts are the most frequently used tools of statistical process control. In their standard form they are designed under the assumption that consecutive observations are statistically independent and described by the normal distribution. W hen these assumptions are not fulfilled statistical properties of the Shewhart control charts are different from those assumed for the design purposes. W hen consecutive observations are not independent the properties of some Shewhart control charts have been investigated only in the case of classic autoregression processes. Hryniewicz (2012) considered the influence of the type of dependence, described in terms of copulas, on the properties of the Shewhart charts for monitoring the mean value of the process. In this paper some results from Hryniewicz (2012) have been recalled. Some new results, obtained for the R-chart used for the control of the variability of a process, have been presented.
PL
Karty kontrolne Shewharta są najczęściej stosowanym narzędziem statystycznego sterowania procesami. W swojej podstawowej postaci są one projektowane przy założeniu, że kolejne obserwacje procesu są statystycznie niezależne, i że są opisane rozkładem normalnym. Jeśli powyższe założenia nie są spełnione, to własności statystyczne kart kontrolnych Shewharta różnią się od tych, które zakłada się w procesie projektowania. Gdy kolejne obserwacje nie są niezależne, własności niektórych kart kontrolnych Shewharta zostały zbadane dla przypadku klasycznych procesów autoregresji. Hryniewicz (2012) rozpatrywał wpływ typu zależności pomiędzy obserwacjami, opisanego za pomocą pojęcia kopuli, na własności karty Shewharta służącej do monitorowania wartości średniej procesu. Niektóre z własności karty Shewharta omawiane w tamtej pracy zostały przypomniane w niniejszym opracowaniu, które zawiera ponadto nowe wyniki dotyczące analogicznego zagadnienia w odniesieniu do karty kontrolnej R, służącej do sterowania zmiennością monitorowanych procesów.
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