Control Chart for Monitoring Variation Using Multiple Dependent State Sampling Under Neutrosophic Statistics

In this paper, a control chart scheme has been introduced for the mean monitoring using gamma distribution for belief statistics using multiple dependent (deferred) state sampling under the neutrosophic statistics. The coefficients of the control chart and the neutrosophic average run lengths have been estimated for specific false alarm probabilities under various process conditions. The offered chart has been compared with the existing classical chart through simulation and the real data. From the comparison, it is concluded that the performance of the proposed chart is better than that of the existing chart in terms of average run length under uncertain environment. The proposed chart has the ability to detect a shift quickly than the existing chart. It has been observed that the proposed chart is efficient in quick monitoring of the out-of-control process and a cherished addition in the toolkit of the quality control personnel.

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A Variable Control Chart Based on Process Capability Index Under Generalized Multiple Dependent State Sampling

IEEE Access ◽

10.1109/access.2019.2903892 ◽

2019 ◽

Vol 7 ◽

pp. 34031-34044 ◽

Cited By ~ 1

Author(s):

G. Srinivasa Rao ◽

Muhammad Ali Raza ◽

Muhammad Aslam ◽

Ali Hussein AL-Marshadi ◽

Chi-Hyuck Jun

Keyword(s):

Control Chart ◽

Process Capability ◽

Process Capability Index ◽

Capability Index ◽

Variable Control ◽

Dependent State

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A Control Chart for COM-Poisson Distribution Using Multiple Dependent State Sampling

Quality and Reliability Engineering International ◽

10.1002/qre.1965 ◽

2016 ◽

Vol 32 (8) ◽

pp. 2803-2812 ◽

Cited By ~ 15

Author(s):

Muhammad Aslam ◽

Liaquat Ahmad ◽

Chi-Hyuck Jun ◽

Osama H. Arif

Keyword(s):

Poisson Distribution ◽

Control Chart ◽

Dependent State

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Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6634887 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Muhammad Aslam ◽

G. Srinivasa Rao ◽

Muhammad Saleem ◽

Rehan Ahmad Khan Sherwani ◽

Chi-Hyuck Jun

Keyword(s):

Control Chart ◽

Control Charts ◽

Average Run Length ◽

Real Life ◽

Quality Characteristics ◽

Statistical Quality Control ◽

Nonnormal Distributions ◽

Run Lengths ◽

Chart Design ◽

Dependent State

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

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Process Monitoring for Gamma Distributed Product under Neutrosophic Statistics Using Resampling Scheme

Journal of Mathematics ◽

10.1155/2021/6635846 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Abdullah M. Almarashi ◽

Muhammad Aslam

Keyword(s):

Control Chart ◽

Simulation Study ◽

Average Run Length ◽

Control Process ◽

Practical Application ◽

Interval Method ◽

Length Measurements ◽

Neutrosophic Statistics ◽

Run Lengths ◽

Different Levels

In this article, a repetitive sampling control chart for the gamma distribution under the indeterminate environment has been presented. The control chart coefficients, probability of in-control, probability of out-of-control, and average run lengths have been determined under the assumption of the symmetrical property of the normal distribution using the neutrosophic interval method. The performance of the designed chart has been evaluated using the average run length measurements under different process settings for an indeterminate environment. In-control and out-of-control nature of the proposed chart under different levels of shifts have been described. The comparison of the proposed chart has been made with the existing chart. A real-world example from the healthcare department has been included for the practical application of the proposed chart. It has been observed from the simulation study and real example that the proposed control chart is efficient in quick monitoring of the out-of-control process. It can be concluded that the proposed control chart can be applied effectively in uncertainty.

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Hotelling T2 chart using the generalized multiple dependent state sampling scheme

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-03-2020-0084 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sandra García-Bustos ◽

Joseph León ◽

María Nela Pastuizaca

Keyword(s):

Control Chart ◽

Average Run Length ◽

Control Process ◽

Sampling Scheme ◽

Real Problem ◽

Optimal Parameters ◽

Content Type ◽

Multivariate Control Chart ◽

Multivariate Control ◽

Dependent State

PurposeThis research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.Design/methodology/approachThis chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.FindingsIt was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.Originality/valueIn this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

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