Economics at your fingertips  

Economic lot sampling inspection from defect counts with minimum conditional value-at-risk

Arturo J. Fernández

European Journal of Operational Research, 2017, vol. 258, issue 2, 573-580

Abstract: Expected cost functions are often minimized to determine optimal inspection schemes for lot acceptance purposes. However, minimum mean cost sampling plans usually have high probabilities of suffering great losses. A risk management perspective based on minimizing the conditional value-at-risk (CVaR) is proposed in order to avoid unacceptably large costs. In essence, the CVaR associated with an inspection scheme is the expected cost of a given proportion of the most costly cases. Optimal defects-per-unit acceptance sampling plans with controlled producer and consumer risks for screening lots of incoming materials and outgoing products are determined by minimizing the CVaR for a given risk aversion degree. The decision criterion is based on the uniformly most powerful test. The Poisson distribution is used to model the number of nonconformities per sampled unit and the natural prior uncertainty on the defect rate Λ is described by a truncated gamma distribution. A computational algorithm is suggested to solve the underlying integer nonlinear programming problem. Practitioners can assume a restricted range for the defect rate and prior knowledge on Λ may be updated using past performance of the inspection plan. The developed methodology is applied to the manufacturing of paper for illustrative purposes.

Keywords: Quality control; Acceptance sampling; Constrained optimization; Integer nonlinear programming; Poisson defect rate (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
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:

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
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:ejores:v:258:y:2017:i:2:p:573-580