Multiattribute bayesian acceptance sampling plans for screening and scrapping rejected lots

Herbert Moskowitz, Robert Plante, Kwei Tang, Arunachalam Ravindran

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

A general model for multiattribute Bayesian acceptance sampling plans is developed which incorporates the multiattribute utility function of a decision maker in its design. The model accommodates various dispositions of rejected lots such as screening and scrapping. The disposition of rejected lots is shown to have a substantial impact on the solution approach used and on the ease of incorporation of multiattribute utility functions in terms of their measurement complexity, functional form, and parameter estimation. For example, if all attributes are screenable upon rejection, and the prior distributions of lot quality on each attribute are independent, then an optimal multiattribute sampling plan can be obtained simply by solving for an optimal single sampling plan on each attribute independently. A discrete search algorithm, based on pattern search, is also developed and shown to be very effective in obtaining an optimal multiattribute inspection plan when such separability cannot be accomplished.

Original languageEnglish (US)
Pages (from-to)185-192
Number of pages8
JournalIIE Transactions (Institute of Industrial Engineers)
Volume16
Issue number2
DOIs
StatePublished - Jan 1 1984

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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