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

Author

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  • Herbert Moskowitz

    (Purdue University)

  • William L. Berry

    (Purdue University)

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.

Suggested Citation

  • Herbert Moskowitz & William L. Berry, 1976. "A Bayesian Algorithm for Determining Optimal Single Sample Acceptance Plans for Product Attributes," Management Science, INFORMS, vol. 22(11), pages 1238-1250, July.
    • Handle: RePEc:inm:ormnsc:v:22:y:1976:i:11:p:1238-1250
    DOI: 10.1287/mnsc.22.11.1238
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    Cited by:

    1. Kwei Tang & Robert Plante & Herbert Moskowitz, 1987. "Stepwise inspection in Bayesian multiattribute acceptance sampling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(4), pages 469-485, August.
    2. Lie‐Fern Hsu & Charles S. Tapiero, 1988. "A bayes approach to quality control of an M/G/1 queue," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(3), pages 327-343, June.
    3. Michael H. Peters & Timothy S. Vaughan, 1992. "Evaluation of an integrated supplier‐buyer quality‐control system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(1), pages 79-96, February.
    4. Carmi, Nava & Ronen, Boaz, 1996. "An empirical application of the information-structures model: The postal authority case," European Journal of Operational Research, Elsevier, vol. 92(3), pages 615-627, August.
    5. George Tagaras & Hau L. Lee, 1987. "Optimal Bayesian single‐sampling attribute plans with modified beta prior distribution," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(6), pages 789-801, December.

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