scholarly journals Group Acceptance Sampling Plan for Resubmitted Lots: Size Biased Lomax Distribution

2021 ◽  
pp. 357-366
Author(s):  
Srinivasa Rao Gadde ◽  
Naga Durgamamba A
Keyword(s):  
Sample Size ◽  
Completion Time ◽  
Sampling Plan ◽  
Acceptance Sampling ◽  
Lomax Distribution ◽  
Minimal Sample ◽  
Acceptance Sampling Plan ◽  
Minimal Sample Size ◽  
Group Acceptance ◽  
Test Completion

This research reveals a group acceptance sampling plan (GASP) for lot resubmitting is designed for conditions wherein an item life is taken from the size biased Lomax distribution (SBLD). The plan parameters of the GASP are obtained by fulfilling the prefixed producer’s and consumer’s risks as per the test completion time and the number of testers. The projected plan needs a minimal sample size in comparison with the standard GASP. This proposed plan is justified with an example.

Group acceptance sampling plan for resubmitted lots based on life tests for odds exponential log logistic distribution

2017 ◽  
Vol 34 (8) ◽  
pp. 1343-1351 ◽  
Author(s):  
Rosaiah K. ◽  
Srinivasa Rao Gadde ◽  
Kalyani K. ◽  
Sivakumar D.C.U.
Keyword(s):  
Group Size ◽  
Sampling Plan ◽  
Logistic Distribution ◽  
Acceptance Sampling ◽  
Consumer’S Risk ◽  
Content Type ◽  
Acceptance Sampling Plan ◽  
Minimum Group ◽  
Termination Time ◽  
Group Acceptance

Purpose The purpose of this paper is to develop a group acceptance sampling plan (GASP) for a resubmitted lot when the lifetime of a product follows odds exponential log logistic distribution introduced by Rao and Rao (2014). The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time. The authors compare the proposed plan with the ordinary GASP, and the results are illustrated with live data example. Design/methodology/approach The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time. Findings The authors determined the group size and acceptance number. Research limitations/implications No specific limitations. Practical implications This methodology can be applicable in industry to study quality control. Social implications This methodology can be applicable in health study. Originality/value The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time.

scholarly journals THE ODD GENERALIZED EXPONENTIAL LOG-LOGISTIC DISTRIBUTION GROUP ACCEPTANCE SAMPLING PLAN

2019 ◽  
Vol 20 (1) ◽  
pp. 103-116
Author(s):  
Devireddy Charana Udaya Sivakumar ◽  
Rosaiah Kanaparthi ◽  
Gadde Srinivasa Rao ◽  
Kruthiventi Kalyani
Keyword(s):  
Sampling Plan ◽  
Logistic Distribution ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Group Acceptance

Two-Stage Group Acceptance Sampling Plan for Burr Type X Percentiles

2013 ◽  
Vol 41 (4) ◽  
pp. 20120209 ◽  
Author(s):  
Muhammad Aslam ◽  
Muhammad Azam ◽  
Y. L. Lio ◽  
Chi-Hyuck Jun
Keyword(s):  
Sampling Plan ◽  
Acceptance Sampling ◽  
Two Stage ◽  
Acceptance Sampling Plan ◽  
Group Acceptance

scholarly journals Group Acceptance Sampling Plan Using Marshall–Olkin Kumaraswamy Exponential (MOKw-E) Distribution

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1066
Author(s):  
Abdullah M. Almarashi ◽  
Khushnoor Khan ◽  
Christophe Chesneau ◽  
Farrukh Jamal
Keyword(s):  
Probability Distributions ◽  
Sampling Plan ◽  
Design Parameters ◽  
Future Research ◽  
Quality Level ◽  
Acceptance Sampling ◽  
Average Life ◽  
Real World Data ◽  
Acceptance Sampling Plan ◽  
Group Acceptance

The current research concerns the group acceptance sampling plan in the case where (i) the lifetime of the items follows the Marshall–Olkin Kumaraswamy exponential distribution (MOKw-E) and (ii) a large number of items, considered as a group, can be tested at the same time. When the consumer’s risk and the test terminsation period are defined, the key design parameters are extracted. The values of the operating characteristic function are determined for different quality levels. At the specified producer’s risk, the minimum ratios of the true average life to the specified average life are also calculated. The results of the present study will set the platform for future research on various nano quality level topics when the items follow different probability distributions under the Marshall–Olkin Kumaraswamy scheme. Real-world data are used to explain the technique.

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