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Minimum cost sampling plans using bayesian methods

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  • Thomas J. Lorenzen

Abstract

This article considers a general method for acceptance/rejection decisions in lot‐by‐lot sampling situations. Given arbitrary cost functions for sampling, accepting, and rejecting (where the cost can depend on the quality of the item) and a prior distribution on supplier quality, formulas are derived that lead to the minimal cost single‐staged inspection plan. For the Bernoulli case, where each item is classified as acceptable or defective, the formulas simplify immensely. A computer code for solving the Bernoulli case is given.

Suggested Citation

  • Thomas J. Lorenzen, 1985. "Minimum cost sampling plans using bayesian methods," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 32(1), pages 57-69, February.
    • Handle: RePEc:wly:navlog:v:32:y:1985:i:1:p:57-69
    DOI: 10.1002/nav.3800320111
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    Cited by:

    1. Hong Wan & Xiaowei Xu & Tian Ni, 2013. "The incentive effect of acceptance sampling plans in a supply chain with endogenous product quality," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(2), pages 111-124, March.
    2. Hau L. Lee & George Tagaras, 1989. "On the robustness of the modified beta distribution for acceptance sampling in statistical quality control," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(4), pages 447-461, August.
    3. 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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