Designing a multiple dependent state sampling plan based on the coefficient of variation

Yan, Aijun; Liu, Sanyang; Dong, Xianguang

doi:10.1186/s40064-016-3087-3

Cited by 30 publications

(12 citation statements)

References 26 publications

(34 reference statements)

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“…The ARL measure for the chart performance is studied by many authors including . The MDS sampling scheme has been utilized in the SPC by many authors including Ahmad et al, Aslam et al, Azam et al, Khan et al, and Knoth …”

Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

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In this article, a new t-chart based on generalized multiple dependent state (GMDS) sampling is proposed for efficient monitoring of a process by assuming that the time between events follows the exponential distribution. The proposed t-chart has two pair of control limits and utilizes the past sample information with the current sample information. The control chart coefficients are estimated by considering different values of the in-control average run lengths. The proposed t-chart is compared with the existing chart by using the out-of-control average run length and extra quadratic loss function. The comparison reveals that the proposed charting strategy has better shift detection ability in process mean. An illustrative example is given for the practical implementation of the proposed chart.

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Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

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“…Followed to this Robab Afshari et.al [4], proposed FMDS for defective items of uncertain proportion and designed the OC curve by using theory of fuzzy mathematics. The quality based coefficient of variation and non-linear optimization model is used to solve the parameters of the developed MDS plan by Yan et al [5]. Based on Bayesian methodology S.Balamurali [6] proposed the MDS plan using Gamma poison distribution.…”

Section: A Review Of the Lecturermentioning

confidence: 99%

Designing Optimal Multiple Deferred State Mds–1 Sampling Plan using Truncated Poisson Distribution

Subramani¹,

Ambika²,

Venugopal³

2019

IJITEE

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The producer and the consumers show their interest in accepting the lot with good quality by checking the lot with minimum sample size. In this paper for given Acceptance Sampling Limit and Limiting Quality Limit we have designed the multiple deferred sampling plans using Truncated Poisson distribution to minimize the risk by considering the sample size in account. Here overall risk for wrong decision is considered as optimization criteria. Tables and procedures are derived using Truncated Poisson distribution and also it is compared with the existing Poisson distribution to find optimum result.

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“…According to Yan et al [4], the relative measure is called the coefficient of variation (CV). This is the ratio of standard deviation (SD), meaning it has been widely used to measure the relative variation to its mean in a variety of fields.…”

Section: Introductionmentioning

confidence: 99%

“…According to Reference [9], "in certain scenarios, the practitioner is not interested in the changes in the mean or the standard deviation but is instead interested in the relative variability compared with the mean". Due to the wide applications of the CV, several authors have designed sampling plans, this includes a proposed multiple dependent state sampling plan for the CV [4], a proposed CV-based plan for two stage sampling [2], and presenting the designing of CV based planning using the repetitive sampling [3].…”

Section: Introductionmentioning

confidence: 99%

Inspection Strategy under Indeterminacy Based on Neutrosophic Coefficient of Variation

Aslam

Aldosari

2019

Symmetry

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The existing sampling plans which use the coefficient of variation (CV) are designed under classical statistics. These available sampling plans cannot be used for sentencing if the sample or the population has indeterminate, imprecise, unknown, incomplete or uncertain data. In this paper, we introduce the neutrosophic coefficient of variation (NCV) first. We design a sampling plan based on the NCV. The neutrosophic operating characteristic (NOC) function is then given and used to determine the neutrosophic plan parameters under some constraints. The neutrosophic plan parameters such as neutrosophic sample size and neutrosophic acceptance number are determined through the neutrosophic optimization solution. We compare the efficiency of the proposed plan under the neutrosophic statistical interval method with the sampling plan under classical statistics. A real example which has indeterminate data is given to illustrate the proposed plan.

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Designing a multiple dependent state sampling plan based on the coefficient of variation

Cited by 30 publications

References 26 publications

Design of a t‐chart using generalized multiple dependent state sampling

Design of a t‐chart using generalized multiple dependent state sampling

Designing Optimal Multiple Deferred State Mds–1 Sampling Plan using Truncated Poisson Distribution

Inspection Strategy under Indeterminacy Based on Neutrosophic Coefficient of Variation

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