Abstract
A methodology for determining optimal sampling plans for Bayesian multiattribute acceptance sampling models is developed. Inspections are assumed to be nondestructive and attributes are classified as scrappable or screenable according to the corrective action required when a lot is rejected on a given attribute. The effects of interactions among attributes on the resulting optimal sampling plan are examined and show that: (1) sampling plans for screenable attributes can be obtained by solving a set of independent single attribute models, (2) interactions of scrappable attributes on screenable attributes and conversely result in smaller sample sizes for screenable attributes than in single attribute plans, and (3) interactions among scrappable attributes result in either smaller sample sizes, lower acceptance probabilities or both, relative to single attribute plans. An iterative subproblem algorithm is developed, which is effective in finding near optimal multiattribute sampling plans having a large number of attributes.
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