Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms

Francisco Aparisi; J. Carlos García-Díaz

doi:10.1016/S0305-0548(03)00099-6

Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms

Francisco Aparisi
, J. Carlos García-Díaz

Polytechnic University of Valencia

Producción: Contribución a una revista › Artículo › revisión exhaustiva

45 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Páginas (desde-hasta)	1437-1454
Número de páginas	18
Publicación	Computers and Operations Research
Volumen	31
N.º	9
DOI	https://doi.org/10.1016/S0305-0548(03)00099-6
Estado	Publicada - ago 2004
Publicado de forma externa	Sí

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title = "Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms",

abstract = "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.",

keywords = "EWMA and MEWMA charts, Genetic algorithms, Optimization, Statistical process control",

author = "Francisco Aparisi and Garc{\'i}a-D{\'i}az, \{J. Carlos\}",

note = "Funding Information: We would like to thank the Foreign Language Co-ordination Office at the Polytechnic University of Valencia for their help in translating this paper. The authors express their thanks for the helpful insights provided by the referees. In addition, the authors acknowledge the financial support of the Ministry of Science and Technology of Spain, Research Project Reference DPI2002-03537.",

year = "2004",

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T1 - Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms

AU - Aparisi, Francisco

AU - García-Díaz, J. Carlos

N1 - Funding Information: We would like to thank the Foreign Language Co-ordination Office at the Polytechnic University of Valencia for their help in translating this paper. The authors express their thanks for the helpful insights provided by the referees. In addition, the authors acknowledge the financial support of the Ministry of Science and Technology of Spain, Research Project Reference DPI2002-03537.

PY - 2004/8

Y1 - 2004/8

N2 - 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.

AB - 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.

KW - EWMA and MEWMA charts

KW - Genetic algorithms

KW - Optimization

KW - Statistical process control

UR - https://www.scopus.com/pages/publications/1842587434

U2 - 10.1016/S0305-0548(03)00099-6

DO - 10.1016/S0305-0548(03)00099-6

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VL - 31

SP - 1437

EP - 1454

JO - Computers and Operations Research

JF - Computers and Operations Research

IS - 9

ER -

Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms

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