Bayesian chain sampling plan based on binomial distribution

Latha, M.; Jeyabharathi, S.

doi:10.1080/09720510.2013.800309

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Vijayaraghavan and Sakthivel [7] have shown Chsp inspection plans with the Bayesian methodology. Latha and Jeyabharathi [8] derived routine measures of Bayesian Chsp using the binomial distribution Devaarul and Moses [9] developed a relational chain sampling plan based on attribute quality features. For the continuous production process, Deva Arul and Senthil Kumar [10] created 2S-VSP with the mean and variance.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing and Selection of Mixed Relational Complete Chain Sampling Plan Using Variance Principle (RCCSP‐VP)

Edna

Joyce

Sagayam

et al. 2023

Mathematical Problems in Engineering

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There are many measurable dimensions for products in industries, and their quality should not deviate too much from the established norm. Therefore, a big part of the consignment’s acceptance is determined by the variance of distributions. In the production of surgical or critical care equipment, measurable dimensions are very important, since each part of the equipment should meet the indicated specification with tolerable variance. Therefore, a procedure for constructing a combined (mixed) sampling plan with a variance measure is provided in this module. This sampling plan supports the medical device engineer in establishing the target dimensions for the quality control of medical instruments because many variations in dimensions result in heavy losses for manufacturers. The relational complete chain sampling plan (RCCSP) is utilized in the II phase inspection to have a significant impact on production quality. The performance measures, algorithm, and design of the mixed sampling plan are provided. The plan is indexed through AQL. In order to choose the plan easily, the table is set up with an example.

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Section: Literature Reviewmentioning

confidence: 99%

Designing and Selection of Mixed Relational Complete Chain Sampling Plan Using Variance Principle (RCCSP‐VP)

Edna

Joyce

Sagayam

et al. 2023

Mathematical Problems in Engineering

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“…Sadeghpour-Gildeh et al Bayesian statistics is based on using prior information in inductive inference and sample data which formally seeks to utilize prior information. Bayesian Acceptance Sampling approach is associated with utilization of prior process history for the selection of appropriate distribution to describe the random fluctuation involved in an Acceptance sampling (Latha and Jeyabharathi 2012 (2013) proposed a decision tree for optimizing the cost acceptance sampling problem based on recursive modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Sequential Sampling Plans Using Dynamic Programming Approach

Nezhad

Momeni²,

Sayani³

et al. 2015

Pak.j.stat.oper.res.

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In this paper we proposed a dynamic programming procedure to develop an optimal sequential sampling plan. A suitable cost model is employed for depicting the cost of sampling, accepting or rejecting the lot. This model is based on sequential approach. A sequential iterative approach is used for modeling the cost of different decisions in each stage. In addition a backward recursive algorithm is developed to solve the dynamic programming. On the other hand, the purpose of this paper is to introduce a new sequential acceptance sampling plan based on dynamic programming. At the end of this paper a numerical example is solved to show how this model works and then sensitivity analyses of main parameters of acceptance sampling model are carried out.

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“…Bayesian sampling schemes require the user to specifically define the distribution from lot to lot of defects. The prior distribution of the sampling plan is the expected distribution of product quality [2]. The mixture of the prior distribution and evidential skill based on the sample information to the decisiveness of the lot.…”

Section: Introductionmentioning

confidence: 99%

Designing Bayesian Two-Sided Group Chain Sampling Plan for Gamma Prior Distribution

Hafeez¹,

Aziz²

2023

Computer Systems Science and Engineering

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Acceptance sampling is used to decide either the whole lot will be accepted or rejected, based on inspection of randomly sampled items from the same lot. As an alternative to traditional sampling plans, it is possible to use Bayesian approaches using previous knowledge on process variation. This study presents a Bayesian two-sided group chain sampling plan (BTSGChSP) by using various combinations of design parameters. In BTSGChSP, inspection is based on preceding as well as succeeding lots. Poisson function is used to derive the probability of lot acceptance based on defective and non-defective products. Gamma distribution is considered as a suitable prior for Poisson distribution. Four quality regions are found, namely: (i) quality decision region (QDR), (ii) probabilistic quality region (PQR), (iii) limiting quality region (LQR) and (iv) indifference quality region (IQR). Producer's risk and consumer's risk are considered to estimate the quality regions, where acceptable quality level (AQL) is associated with producer's risk and limiting quality level (LQL) is associated with consumer's risk. Moreover, AQL and LQL are used in the selection of design parameters for BTSGChSP. The values based on all possible combinations of design parameters for BTSGChSP are presented and inflection points' values are found. The finding exposes that BTSGChSP is a better substitute for the existing plan for industrial practitioners.

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Bayesian chain sampling plan based on binomial distribution

Cited by 4 publications

References 2 publications

Designing and Selection of Mixed Relational Complete Chain Sampling Plan Using Variance Principle (RCCSP‐VP)

Designing and Selection of Mixed Relational Complete Chain Sampling Plan Using Variance Principle (RCCSP‐VP)

Optimal Sequential Sampling Plans Using Dynamic Programming Approach

Designing Bayesian Two-Sided Group Chain Sampling Plan for Gamma Prior Distribution

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