Volume 13, Issue 2 (8-2016)                   2016, 13(2): 53-68 | Back to browse issues page

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Optimum Fuzzy Non-parametric Predictive Inference for Single Acceptance Sampling. Journal of Operational Research and Its Applications. 2016; 13 (2) :53-68
URL: http://jamlu.liau.ac.ir/article-1-1376-en.html
Abstract:   (3657 Views)

Acceptance sampling is one of the main parts of the statistical quality control. It is primarily used for the inspection of incoming or outgoing lots. Acceptance sampling procedures can be used in an acceptance control program to reach better quality with lower expenses, improvement of the control and the increase of efficiency. The aims of this paper, studying acceptance sampling based on non-parametric predictive inference in a fuzzy environment. In some cases, it may not be possible to define acceptance sampling parameters as crisp values. Especially in production environments, it may not be easy to define the parameters number of conforming items and the size of the samples crisp values. In these cases, these parameters can be expressed by linguistic variables. The fuzzy set theory can be used successfully to cope the vagueness in these linguistic expressions for acceptance sampling based on non-parametric predictive inference. In other words, the aim of this paper is present a new method titled fuzzy non-parametric predictive inference for single acceptance sampling plan

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Type of Study: Research | Subject: Special
Received: 2016/11/1 | Accepted: 2016/11/1 | Published: 2016/11/1

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