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Akbar Asgharzadeh

Akbar Asgharzadeh

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Faculty of Mathematical Sciences
Address: Department of Statistics University of Mazandaran Babolsar, IRAN
Phone: 011-54302476

Research

Title
Optimal truncated repetitive lot inspection with defect rates
Type
JournalPaper
Keywords
Producer and consumer risks Average, sample number, Poisson distribution, Integer nonlinear programming
Year
2019
Journal APPLIED MATHEMATICAL MODELLING
DOI
Researchers Carlos Pérez-González ، Arturo Fernadez ، Akram Kohansal ، Akbar Asgharzadeh

Abstract

Single and repetitive sampling schemes are conventional methods for evaluating the qual- ity of lots or batches of products. Truncation repetitive sampling plans are introduced in this paper in order to significantly reduce the size of samples drawn from the lot. Under this scheme type, if a decision about the acceptance or rejection of the lots cannot be made in the original inspection, they can be reinspected, at most, a prefixed number of times. The Poisson distribution is assumed for the number of defects found in the samples drawn from the lot. Best truncated repetitive sampling plans are determined by solving integer nonlinear programming problems. A simplified methodology is suggested to obtain the plans with optimal inspection effort and controlled risks by using an iterative procedure. According to the results obtained, optimal truncated plans are shown to be better than the optimal single and repetitive schemes in reducing the average sample number of the inspection. An application to the manufacturing of glass is presented for illustrative purposes.