A Bayesian Algorithm for Determining Optimal Single Sample Acceptance Plans for Product Attributes

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Abstract
The Bayesian algorithm presented in this paper provides a generalized procedure for determining the minimum cost sample size (n*) and acceptance number (c*) for single sample attribute acceptance plans. The algorithm is applicable to a broad range of acceptance sampling problems, assuming only that the distributions of product quality are discrete, and that the sampling cost is either a linear or strictly convex function of the sample size. Experimental results are presented that compare the solution quality and the computational requirements of this algorithm with three types of previously reported procedures: (1) Bayesian decision tree methods, (2) analytic approximation methods, and (3) direct search techniques. The results indicate that the algorithm produces the optimal solution with minimal computational requirements over a wide range of acceptance sampling problem types.

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