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Multiple Deferred State Sampling Plan with Fuzzy Parameter
by
Afshari, Robab
, Sadeghpour Gildeh, Bahram
, Sarmad, Majid
in
Artificial Intelligence
/ Binomial distribution
/ Computational Intelligence
/ Decision making
/ Engineering
/ Fuzzy sets
/ Management Science
/ Operations Research
/ Parameters
/ Probability theory
/ Random variables
/ Sample size
/ Sampling
2018
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Multiple Deferred State Sampling Plan with Fuzzy Parameter
by
Afshari, Robab
, Sadeghpour Gildeh, Bahram
, Sarmad, Majid
in
Artificial Intelligence
/ Binomial distribution
/ Computational Intelligence
/ Decision making
/ Engineering
/ Fuzzy sets
/ Management Science
/ Operations Research
/ Parameters
/ Probability theory
/ Random variables
/ Sample size
/ Sampling
2018
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Do you wish to request the book?
Multiple Deferred State Sampling Plan with Fuzzy Parameter
by
Afshari, Robab
, Sadeghpour Gildeh, Bahram
, Sarmad, Majid
in
Artificial Intelligence
/ Binomial distribution
/ Computational Intelligence
/ Decision making
/ Engineering
/ Fuzzy sets
/ Management Science
/ Operations Research
/ Parameters
/ Probability theory
/ Random variables
/ Sample size
/ Sampling
2018
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Multiple Deferred State Sampling Plan with Fuzzy Parameter
Journal Article
Multiple Deferred State Sampling Plan with Fuzzy Parameter
2018
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Overview
Traditional multiple deferred state sampling plan by attribute is an inspection with crisp parameter. In this plan, in addition to using the current lot information, the future successive lots information can also be utilized on sentencing the current lot. In this study, a fuzzy multiple deferred state (FMDS) sampling plan by attribute is proposed when the proportion of defective items (
p
) is uncertain. So, the fuzzy probability theory is applied to construct the operating characteristic curve of mentioned plan. Then, we concentrate more on a special feature of FMDS(0,1,2) plan. We show that the proposed plan is well-defined because it converts to classic case when
p
is not imprecise. In order to be fully informed, some examples will also be discussed. The comparisons between the FMDS(0,1,2) plan and the existing fuzzy single sampling plan (FSSP) with zero and one acceptance number show that the proposed plan is more powerful than the FSSP in distinguishing good and bad quality of the lot. It also enjoys the least sum of the consumer and producer risks.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
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