An Economic Design of Rectifying Double Acceptance Sampling Plans via Maxima Nomination Sampling
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Mansooreh Razmkhah
und
Jafar Ahmadi
Abstract
In industry when a lot of items is sent for inspection, double acceptance sampling plans (DASP) are considered as a way to decide on acceptance or rejection of the lot. If the lot contains items with high sensitivity, then the measuring of quality characteristics is destructive or costly. So we are looking for a method to decide that it has high performance. Using the ranked set sampling (RSS) method will make it stricter and more accurate whether or not to accept a lot. Moreover, it is affordable and will not burden extra costs on the buyer or the producer. In this paper, by using a special type of RSS, with the name of maxima nomination sampling (MNS), we design a DASP with regards to the total loss function. The results indicate that the total loss function, which is acquired by the MNS method, has lower values than the one using the simple random sampling (SRS) method.
Acknowledgements
The authors would like to thank the Editor-in-Chief for his comments on the previous version of the paper which led to a significant improvement of that version.
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© 2017 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Multi-Type Branching Processes Modeling of Nosocomial Epidemics
- Estimation of Reliability in Multicomponent Stress-Strength Based on Dagum Distribution
- Bootstrap Lower Confidence Limits of Superstructure Process Capability Indices for Esscher-Transformed Laplace Distribution
- An Economic Design of Rectifying Double Acceptance Sampling Plans via Maxima Nomination Sampling
Artikel in diesem Heft
- Frontmatter
- Multi-Type Branching Processes Modeling of Nosocomial Epidemics
- Estimation of Reliability in Multicomponent Stress-Strength Based on Dagum Distribution
- Bootstrap Lower Confidence Limits of Superstructure Process Capability Indices for Esscher-Transformed Laplace Distribution
- An Economic Design of Rectifying Double Acceptance Sampling Plans via Maxima Nomination Sampling
