@article{49c2eef880fe47c6a13fa9f0f91e29d6,
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",
month = aug,
doi = "10.1016/S0305-0548(03)00099-6",
language = "English",
volume = "31",
pages = "1437--1454",
journal = "Computers and Operations Research",
issn = "0305-0548",
publisher = "Elsevier Ltd.",
number = "9",
}