A Bayesian acceptance sampling scheme when utilities are piecewise constant and the sampling cost is linear

Optimization ◽  
1975 ◽  
Vol 6 (5) ◽  
pp. 797-807
Author(s):  
I.G. Evans
Keyword(s):  
Sampling Scheme ◽  
Acceptance Sampling ◽  
Piecewise Constant ◽  
Sampling Cost

Acceptance Sampling Scheme with Warranty for Life Testing Using Bayesian Inference Methods

2020 ◽  
Vol 49 (5) ◽  
pp. 20180476
Author(s):  
Jianping Zhu ◽  
Junge Sun ◽  
Hua Xin ◽  
Chenlu Zheng ◽  
Tzong-Ru Tsai
Keyword(s):  
Bayesian Inference ◽  
Sampling Scheme ◽  
Acceptance Sampling ◽  
Life Testing ◽  
Inference Methods

A flexible process-capability-qualified resubmission-allowed acceptance sampling scheme

2015 ◽  
Vol 80 ◽  
pp. 62-71 ◽  
Author(s):  
Chien-Wei Wu ◽  
Ming-Hung Shu ◽  
Alexander A. Nugroho ◽  
Nani Kurniati
Keyword(s):  
Process Capability ◽  
Sampling Scheme ◽  
Acceptance Sampling

The economic design of variable acceptance sampling plan with rectifying inspection

Kybernetes ◽  
2015 ◽  
Vol 44 (3) ◽  
pp. 440-450 ◽  
Author(s):  
Ching-Ho Yen ◽  
Heng Ma ◽  
Chi-Huang Yeh ◽  
Chia-Hao Chang
Keyword(s):  
Sampling Plan ◽  
Acceptance Sampling ◽  
Total Quality ◽  
Quality Cost ◽  
Content Type ◽  
Acceptance Sampling Plan ◽  
Variable Sampling ◽  
Capability Indices ◽  
Sampling Cost ◽  
The One

Purpose – The purpose of this paper is to develop an economic model, which could determine the acceptance sampling plan that minimizes the quality cost for batch manufacturing. Design/methodology/approach – The authors propose a variable sampling plan based on one-sided capability indices for dealing with the quality cost requirement. Findings – The total quality cost is much more sensitive to process capability indices and inspected cost than internal and external failure costs. Research limitations/implications – The experimental data were randomly generated instead of real world ones. Practical implications – The proposed model is specifically designed for manufacturing industries with high sampling cost. Originality/value – The one-sided capability indices were utilized for the first time to be suitable for the purpose.

scholarly journals Design of economically optimal zero-defect acceptance sampling with rectification when diagnosis errors are present

2005 ◽  
Vol 25 (1) ◽  
pp. 29-44
Author(s):  
Roberto da Costa Quinino ◽  
Linda Lee Ho ◽  
Emílio Suyama
Keyword(s):  
Sampling Scheme ◽  
Acceptance Sampling ◽  
Misclassification Errors ◽  
Optimum Sampling ◽  
Diagnosis Errors

In this paper we present the optimum sampling size in zero-defect acceptance with rectification sampling scheme in the presence of misclassification errors. Its development is based on an economical model. The procedures are implemented in a program using the software Matlab and illustrated by an example.

The α-Optimal Acceptance Sampling Scheme

1986 ◽  
Vol 18 (1) ◽  
pp. 63-66 ◽  
Author(s):  
Elart Von Collani
Keyword(s):  
Sampling Scheme ◽  
Acceptance Sampling

Optimal Experimental Design With Nesting of Persons in Organizations

2013 ◽  
Vol 221 (3) ◽  
pp. 145-159 ◽  
Author(s):  
Gerard J. P. van Breukelen
Keyword(s):  
Repeated Measures ◽  
Optimal Number ◽  
Health Centers ◽  
Randomized Experiments ◽  
Optimal Sample ◽  
Treatment Conditions ◽  
Sampling Cost ◽  
Nested Designs ◽  
Simple Equations ◽  
Number Of Individuals

This paper introduces optimal design of randomized experiments where individuals are nested within organizations, such as schools, health centers, or companies. The focus is on nested designs with two levels (organization, individual) and two treatment conditions (treated, control), with treatment assignment to organizations, or to individuals within organizations. For each type of assignment, a multilevel model is first presented for the analysis of a quantitative dependent variable or outcome. Simple equations are then given for the optimal sample size per level (number of organizations, number of individuals) as a function of the sampling cost and outcome variance at each level, with realistic examples. Next, it is explained how the equations can be applied if the dependent variable is dichotomous, or if there are covariates in the model, or if the effects of two treatment factors are studied in a factorial nested design, or if the dependent variable is repeatedly measured. Designs with three levels of nesting and the optimal number of repeated measures are briefly discussed, and the paper ends with a short discussion of robust design.

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