Selection of Bayesian Multiple Deferred Sampling Plan (BMDS-1) with Weighted Poisson Model Based on AOQ and ATI Curve

Subbiah, Krishnaveni; Latha, M.

doi:10.9734/jsrr/2016/30036

Cited by 4 publications

(5 citation statements)

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“…Average probability of acceptance for BNTSGChSP based on beta and binomial distributions is obtained by substituting Equations ( 8) and ( 9) into Equation (10).…”

Section: 𝐿 (𝑝) = 𝑃𝑃𝑃 + 3𝑃𝑃𝑄mentioning

confidence: 99%

“…9 By using AQL and LQL a SSP was modified to a Bayesian SSP for Poisson distribution. 10,11,12 Group acceptance sampling plan (GASP) was introduced by Aslam and Jun. 13 GASP is used to check multiple products simultaneously when a larger number of testers are available for inspection.…”

Section: Introductionmentioning

confidence: 99%

“…They further extend their plan for the Bayesian chain sampling plan (BChSP‐1) for construction and performance measures 8 and by using quality regions 9 . By using AQL and LQL a SSP was modified to a Bayesian SSP for Poisson distribution 10,11,12 …”

Section: Introductionmentioning

confidence: 99%

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Designing Bayesian new two‐sided group chain sampling plan for quality regions based on beta prior

Hafeez

Aziz

2023

Quality & Reliability Eng

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Acceptance sampling is used to determine whether to accept or reject the lot that is being inspected. The Bayesian strategy is the ideal method to use when historical data about the product is available in order to make decision. To estimate quality regions, we suggest a Bayesian new two‐sided group chain sampling plan in this study. The operating characteristic function is developed using the binomial distribution, and average probability of acceptance is estimated using beta distribution as prior. Four quality regions are estimated for certain predetermined values of producer's and consumer's risks. Producer's risk and consumer's risk are correlated with acceptable quality level and limiting quality level of quality regions, respectively. For the proposed plan, inflection point values are estimated based on various combinations of design parameters. Operating characteristic curves are use up to compare the current Bayesian group chain sampling plan (BGChSP) with the effects of changing the values of specified parameter values. The suggested plan is a well replacement for current industry plans, according to the operating characteristic curve's findings.

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“…Average probability of acceptance for BNTSGChSP based on beta and binomial distributions is obtained by substituting Equations ( 8) and ( 9) into Equation (10).…”

Section: 𝐿 (𝑝) = 𝑃𝑃𝑃 + 3𝑃𝑃𝑄mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Designing Bayesian new two‐sided group chain sampling plan for quality regions based on beta prior

Hafeez

Aziz

2023

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Hald [12] provides a system of attribute single sampling plan obtained by minimizing average cost. Later, single sampling plan was modified by using acceptable quality level (AQL) and limiting quality level (LQL) for Bayesian single sampling plan with weighted Poisson distribution [13] and Poisson distribution [14]. Later, in the construction and performance measure, a Bayesian chain sampling plan was discussed for gamma prior [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian Group Chain Sampling Plan for Poisson Distribution with Gamma Prior

Hafeez¹,

Aziz²,

Zain³

et al. 2022

Computers, Materials &Amp; Continua

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Acceptance sampling is a statistical quality control technique that consists of procedures for sentencing one or more incoming lots of finished products. Acceptance or rejection is based on the inspection of sampled products drawn randomly from the lot. The theory of previous acceptance sampling was built upon the assumption that the process from which the lots are produced is stable and the process fraction nonconforming is a constant. Process variability is inevitable due to random fluctuations, which may inadvertently lead to quality variation. As an alternative to traditional sampling plans, Bayesian approach can be used by considering prior information of the process. Using different combinations of design parameters, this study introduces a Bayesian group chain sampling plan (BGChSP). For the first time in group chain sampling plan, the probability of lot acceptance is derived by using Poisson distribution to estimate an average number of defectives. Gamma distribution is used as a prior distribution with Poisson distribution. Taking into account both consumer's and producer's risks, this research considers two quality regions namely, probabilistic quality region (PQR) and indifference quality region (IQR). By minimizing consumer's and producer's risks, BGChSP can be used to minimize the average number of defective products in industry.

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“…A single sampling plan was modified by using the acceptable quality limit (AQL) and limiting quality level (LQL). The Bayesian single sampling plan was considered, with weighted Poisson distribution [11,12] and Poisson distribution [13]. Latha et al [14] used the gamma prior and proposed Bayesian chain sampling plan for construction and performance measures.…”

Section: Introductionmentioning

confidence: 99%

Designing Bayesian New Group Chain Sampling Plan For Quality Regions

Hafeez¹,

Aziz²,

Zain³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Acceptance sampling is a well-established statistical technique in quality assurance. Acceptance sampling is used to decide, acceptance or rejection of a lot based on the inspection of its random sample. Experts concur that the Bayesian approach is the best approach to make a correct decision, when historical knowledge is available. This paper suggests a Bayesian new group chain sampling plan (BNGChSP) to estimate average probability of acceptance. Binomial distribution function is used to differentiate between defective and non-defective products. Beta distribution is considered as a suitable prior distribution. Derivation is completed for the estimation of the average proportion of defectives. This study includes four quality regions namely: (i) probabilistic quality region (PQR), (ii) quality decision region (QDR), (iii) limiting quality region (LQR), and (iv) indifference quality region (IQR). The estimated values for the BNGChSP are tabulated and the inflection point values are derived, based on different combinations of design parameters including both consumer's and producer's risks. For comparison with the existing plan, the operating characteristic curves expose that BNGChSP is a better substitute for industrial practitioners.

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Selection of Bayesian Multiple Deferred Sampling Plan (BMDS-1) with Weighted Poisson Model Based on AOQ and ATI Curve

Cited by 4 publications

References 0 publications

Designing Bayesian new two‐sided group chain sampling plan for quality regions based on beta prior

Designing Bayesian new two‐sided group chain sampling plan for quality regions based on beta prior

Bayesian Group Chain Sampling Plan for Poisson Distribution with Gamma Prior

Designing Bayesian New Group Chain Sampling Plan For Quality Regions

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