Group acceptance sampling plans based on time truncated life tests for compound Weibull-exponential distribution

PurposeAcceptance sampling plans are a decision-making process on the basis of a randomly selected sampling from a party, where it is not possible to completely scan the products for reasons such as time and cost being limited or the formation of damaged products during the inspection. For some products, the life span (time from beginning to failure) may be an important quality characteristic. In this case, the quality control adequacy of the products can be checked with an acceptance sampling plan based on the truncated life test with a censored scheme for the lifetime of the products. In this study, group acceptance sampling plans (GASPs) based on life tests are studied under the Type-I censored scheme for the compound Weibull-exponential (CWE) distribution.Design/methodology/approachGASPs based on life tests under the Type-I censored scheme for the CWE distribution are developed by using both the producer's risk and the consumer's risk.FindingsIn this study, optimum sample size, optimum number of groups and acceptance number are obtained under the Type-I censored scheme for the CWE distribution. Real data set illustration is given to show GASPs how to be used for the industry applications.Originality/valueDifferent from acceptance sampling plans with just considering the producer's risk, GASPs are constructed by using two-point approach included both the producer's risk and the consumer's risk for CWE distribution.

Odd generalized exponential log logistic distribution: A new acceptance sampling plans based on percentiles

<span>In this paper, acceptance sampling plans are developed for the odd generalized exponential log logistic distribution based on percentiles when the life test is truncated at a pre-specified (pre-determined) time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given consumer’s risk. The operating characteristic values of the sampling plans as well as the producer’s risk are presented. One example with real data set is also given as an illustration.</span>

Mathematical Nonlinear Goal Programming in Quality Control

This paper concerned with applying suggested mathematical programming and nonlinear goal programming models to determine the producer's risk (α), consumer's risk (β) and acceptance level (c) simultaneously. The suggested nonlinear goal programming model allowed α and β values to be free and determined their values more accurately which make balance between the power of a statistical test (1-β) and level of significance α. Real quality control data are used to evaluate the performance of the suggested models . This enables decision makers in quality control to develop more accurate and free acceptance sampling plans.

Selection of Tightened-Normal-Tightened sampling scheme under the implications of intervened Poisson distribution

Tightened-normal-tightened (TNT) sampling scheme is one of the most frequently used sampling schemes for making decisions about the finished product lots by examining certain samples from the lots. TNT sampling scheme includes two attribute sampling plans, one for tightened inspection and other for normal inspection along with switching rules. This paper introduces a procedure for TNT by incorporating two single sampling plans (SSP) under the conditions of intervened Poisson distribution (IPD) for the lots which may have a possibility of some intervention during the production process. The paper also assesses the performance of the proposed scheme procedure through its operating characteristic curves. Also, the unity value table is provided for certain parameters of specified producer’s risk and consumer’s risk for shop floor conditions. Further, the efficiency of proposed TNT scheme over the individual SSP under the conditions of IPD is demonstrated with illustrations.

A New Acceptance Sampling Plans Based on Percentiles for Odds Exponential Log Logistic Distribution

In this paper, acceptance sampling plans are developed for the odds exponential log logistic distribution (OELLD) introduced by Rosaiah et al. [1] based on lifetime percentiles when the life test is truncated at a predetermined time. The minimum sample size necessary to ensure the specified lifetime percentile is obtained under a given customer’s risk. The operating characteristic values of the sampling plans as well as the producer’s risk are presented. One example with real data set is also given as an illustration.

Double Acceptance Sampling Plans Based on Truncated Life Tests for the Marshall-Olkin Extended Exponential Distribution

In this paper, double acceptance sampling plans are developed for a truncated life test, when the lifetime of an item follows the Marshall-Olkin extended exponential distribution. The probability of acceptance is calculated for different consumer’s confidence levels fixing the producer’s risk at 0.05. The probability of acceptance and the producer’s risk are explained by means of examples.

New Acceptance Sampling Plans Based on Percentiles for Exponentiated Fréchet Distribution

In this article, acceptance sampling plans are developed for the exponentiated Fréchet distribution based on percentiles when the life test is truncated at a pre-specified time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given customer's risk and producer's risk simultaneously. The operating characteristic values of the sampling plans are presented. One example with real data set is also given as an illustration.

Design of SkSP-R Variables Sampling Plans

In this paper, we present the designing of the skip-lot sampling plan including the re-inspection called SkSP-R. The plan parameters of the proposed plan are determined through a nonlinear optimization problem by minimizing the average sample number satisfying both the producer's risk and the consumer's risks. The proposed plan is shown to perform better than the existing sampling plans in terms of the average sample number. The application of the proposed plan is explained with the help of illustrative examples.