Selection of Combined Continuous Lot-by-Lot Sampling Plans with Single Sampling Plan by Attributes as the Reference Plan

2014 ◽  
Vol 42 (4) ◽  
pp. 20130193
Author(s):  
R. Vijayaraghavan ◽  
P. Aruna
2015 ◽  
Vol 4 (1) ◽  
pp. 43-59 ◽  
Author(s):  
Sampath Sundaram ◽  
Lalitha Singhan Madhavachari ◽  
Ramya Balu

This paper considers the design of single sampling plan for variables when the experimental values are treated as observations on independently and identically distributed normal random variables with fuzzy mean value. The design makes use of Liu's (2008) model IV of Chance Theory. Sampling Plans determined by the sample sizes and acceptance numbers are constructed for situations involving imprecise parameter on using chance theory. The process of determining sample sizes and acceptance threshold values has been carried out on assuming the observed values have hybrid normal distribution. Optimal chance sampling plans for variables are also determined by using minimum angle criterion for different choices of fuzzy risks.


2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Saminathan Balamurali ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun

Skip-lot sampling plans have been widely used in industries to reduce the inspection efforts when products have good quality records. These schemes are known as economically advantageous and useful to minimize the cost of the inspection of the final lots. A new system of skip-lot sampling plan called SkSP-R is proposed in this paper. The performance measures for the proposed SkSP-R plan are derived using the Markov chain formulation. The proposed plan is found to be more efficient than the single sampling plan and the SkSP-2 plan.


2008 ◽  
Vol 35 (2) ◽  
pp. 149-160 ◽  
Author(s):  
R. Vijayaraghavan ◽  
K. Rajagopal ◽  
A. Loganathan

2019 ◽  
Vol 8 (4) ◽  
pp. 10110-10119

This article explores the problem of investigate Single Sampling Plan (SSP) by attributes under Bayesian theory and illuminate its importance methodology in manufacturing industries. The modern technological advancements and well monitoring of the production process are facilitate to enhance the standard of product. In such situation products are not meeting the specified quality standards is a rare phenomenon. However, random fluctuations in producing processes might lead some merchandise to an imperfect quality. It has been assumed that the number of defects per unit of product follows a Zero Inflated Poisson distribution (ZIP) and the Gamma distribution is the conjugate prior to the average number of non-conformities per item. This article proposed a new sampling procedure as Bayesian Single Sampling plan (BSSP) using Gamma-Zero Inflated Poisson (G-ZIP) distribution. Necessary tables for the selection of optimal plan parameters and numerical illustrations were made for this sampling plan. Furthermore, the applicability and usefulness of the proposed Bayesian sampling plan under the G-ZIP model have been demonstrated by a few examples and comparisons were made with other sampling plans.


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