EconPapers    
Economics at your fingertips  
 

Developing optimal group acceptance sampling plans based on Weibull distribution with limited risks

M. Naghizadeh Qomi and Muhammad Aslam

Journal of Applied Statistics, 2026, vol. 53, issue 7, 1237-1252

Abstract: Single and group sampling plans are conventional methods for assessing the quality of submitted lots of products. The group scheme is considered as more efficient than the single sampling plan with respect to cost and time to reach the final decision about the submitted lot. In this paper, a group sampling plan for the lot acceptance is developed for the time-truncated life test when the lifetime of a product follows the Weibull distribution. The test plans are constructed by minimizing and limiting a weighted-average of the producer and consumer risks. Integer nonlinear programing is used to determine the optimal number of groups and the acceptance number. Several tables and figures are constructed to analyze the behavior of the proposed test plans. A comparison of the suggested GASP and the traditional optimal two-point plan shows that the proposed optimal plans outperform the optimal two-point plan in terms of sample size. A real data analysis is presented for illustration of the results. The results derived for Weibull distribution are also valid for any other lifetime model.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2025.2555591 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:53:y:2026:i:7:p:1237-1252

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2025.2555591

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2026-07-02
Handle: RePEc:taf:japsta:v:53:y:2026:i:7:p:1237-1252