Efficient truncated repetitive lot inspection using Poisson defect counts and prior information

Carlos J. Pérez-González, Arturo J. Fernández and Akram Kohansal

European Journal of Operational Research, 2020, vol. 287, issue 3, 964-974

Abstract: Optimal truncated repetitive sampling plans for inspecting lots of manufactured material using Poisson defect count data and prior information are obtained by minimizing the expected sampling effort. The proposed plans outperform the repetitive and single inspection schemes and they are shown to be better in reducing the number of sample units drawn from the lot. In truncated repetitive plans, the lots can be reinspected, at most, an optimal number of times when their acceptance or rejection cannot be concluded from the original inspection. Assuming a gamma prior model to describe the uncertainty about the unknown defect rate, a computational procedure is proposed to determine the truncated repetitive sampling plans with minimum expected sampling effort by solving integer nonlinear programming problems. The inclusion of lot reinspections, as well as previous information into the decision criteria, also provides the practitioners with a more precise evaluation of the expected producer’s and consumer’s risks. The results obtained illustrate that truncated repetitive plans with expected risks produce important savings in inspection time and cost with respect to conventional sampling schemes based on the classical requirements of quality levels and risks. An application concerning the manufacturing of glass is provided for illustrative purposes.

Keywords: Quality control; Expected producer and consumer risks; Expected average sample number; Poisson distribution; Gamma prior model (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221720304574
Full text for ScienceDirect subscribers only

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:eee:ejores:v:287:y:2020:i:3:p:964-974

DOI: 10.1016/j.ejor.2020.05.027

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().