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Designing Economic Inspection Sampling Procedure with Bayesian Approach

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Abstract:

This paper proposes a Bayesian inspection sampling model for single specification limit components with the objective of minimizing the expected total cost. The model assumes that the producer will undertake a two-stage Bayesian inspection process for the purchased lot: (1) determine the optimal sample size, and (2) after learning the sampling information, decide to either inspect all of the remainder or stop inspections. In particular, the paper studies the case where the quality characteristic of the component has a location-shifting exponential distribution with an unknown mean. Using gamma distribution as the conjugate prior, a closed form decision criterion for the second stage inspection process can be derived, and the computational complexity will be greatly reduced. The attribute sampling model under the same probability distribution and cost structure is also deduced and compared with the original model. Finally, an electronic lighting device example is used to illustrate the proposed model, and the effects of the model parameters are discussed.

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Periodical:

Advanced Materials Research (Volume 711)

Pages:

683-688

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.711.683

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Cite this paper

Online since:

June 2013

Authors:

I Ping Huang, Chiuh Cheng Chyu

Keywords:

Attribute Sampling Plan, Bayesian Decision Analysis, Single Specification Limit, Variable Sampling Plan

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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