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Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms [An article from: Computers and Operations Research]
Book Details
Author(s)
F. Aparisi, J. Carlos Garci@?a-Di@?az
Publisher
Elsevier
ISBN / ASIN
B000RR16QS
ISBN-13
978B000RR16Q3
Availability
Available for download now
Sales Rank
#10,691,620
Marketplace
United States 🇺🇸
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Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Exponentially weighted moving-average (EWMA) and multivariate EWMA (MEWMA) process control charts can be applied to detect small changes in statistical process control efficiently. This paper presents a software program developed in Windows environment for the optimal design of the EWMA and MEWMA chart parameters, to protect the process in the case of shifts of given size. Optimization has been done using genetic algorithms.
Description:
Exponentially weighted moving-average (EWMA) and multivariate EWMA (MEWMA) process control charts can be applied to detect small changes in statistical process control efficiently. This paper presents a software program developed in Windows environment for the optimal design of the EWMA and MEWMA chart parameters, to protect the process in the case of shifts of given size. Optimization has been done using genetic algorithms.
