Authors:
Seyed Taghi Akhavan Niaki
and
Paravaneh Jahani
Affiliation:
Sharif University of Technology, Iran, Islamic Republic of
Keyword(s):
Multiattribute processes, Economic design, Multivariate exponentially weighted moving average chart, Variable Sample Size, Variable Sampling Interval, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Industrial Engineering
;
Methodologies and Technologies
;
Operational Research
Abstract:
In this research, a new methodology is developed to economically design a multivariate exponentially weighted moving average (MEWMA) control chart for multiattribute processes. The optimum design parameters of the chart, i.e., the sample size, the sampling interval, and the warning/action limit coefficients, are obtained using a genetic algorithm to minimize the expected total cost per hour. A sensitivity analysis has also been carried out to investigate the effects of the cost and model parameters on the solutions obtained.