Sequential variable sampling plan for normal distribution

In this paper, a sequential variable sampling plan is studied. Suppose that the quality of an item in a batch is measured by a normally distributed random variable with a known variance, but the mean is unknown with a normal prior distribution. Then by using Bayesian approach and considering a Marko...

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Bibliographic Details
Published in:European journal of operational research 2006-07, Vol.172 (1), p.127-145
Main Authors: Lam, Yeh, Li, Kim-Hung, Ip, Wai-Cheung, Wong, Heung
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Bayesian analysis
Decision theory. Utility theory
Exact sciences and technology
Flows in networks. Combinatorial problems
Markov decision process
Operational research and scientific management
Operational research. Management science
Operations research
Optimality equation
Random variables
Sampling
Sequential sampling plans
Statistical methods
Studies
ϵ-optimal decision
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Summary:In this paper, a sequential variable sampling plan is studied. Suppose that the quality of an item in a batch is measured by a normally distributed random variable with a known variance, but the mean is unknown with a normal prior distribution. Then by using Bayesian approach and considering a Markov decision process, the optimality equations for the minimum total expected cost are formulated. We show that an optimal decision rule will have a control limit structure. An algorithm for a sequence of ϵ-optimal decisions is introduced. Then, the statistical procedure for conducting the sequential sampling plan is presented.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2004.09.034