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

Research output: Contribution to journal › Article › peer-review

45 Scopus citations

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.

Original language	English
Pages (from-to)	1437-1454
Number of pages	18
Journal	Computers and Operations Research
Volume	31
Issue number	9
DOIs	https://doi.org/10.1016/S0305-0548(03)00099-6
State	Published - Aug 2004
Externally published	Yes

Keywords

EWMA and MEWMA charts
Genetic algorithms
Optimization
Statistical process control

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10.1016/S0305-0548(03)00099-6

Cite this

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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.",

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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

Abstract

Keywords

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