X charts with variable sampling intervals
Technometrics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A genetic algorithm for the vehicle routing problem
Computers and Operations Research
An improved genetic algorithm for facility layout problems having inner structure walls and passages
Computers and Operations Research
A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling
Computers and Operations Research
Fuzzy control of pH using genetic algorithms
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A genetic algorithm approach to determine the sample size for attribute control charts
Information Sciences: an International Journal
Computers and Industrial Engineering
Optimal linear combination of Poisson variables for multivariate statistical process control
Computers and Operations Research
Hi-index | 12.05 |
Hotelling's T^2 chart is the most widely used multivariate procedure for two or more related quality characteristics, but it is slow in detecting small process shifts. Recently, the variable sampling interval (VSI) control scheme in which the sampling interval between two success sampling points is varied based on the preceding T^2 value has been shown to provide an increase to the detecting efficiency of the original T^2 chart. In this paper a method is proposed to conduct the economic design of the VSI T^2 chart when the in-control process mean vector and covariance matrix are unknown. It is assumed that only one assignable cause of variation exists and the time between occurrences is exponentially distributed. Accordingly, the Markov Chain approach is allowable to develop a cost model. In applying genetic algorithms to minimize the cost function, the optimal values of sample size, sampling interval lengths, upper control limit and warning limit used to choose one of the sampling interval lengths can be determined. Variable sampling interval and original T^2 charts are compared with respect to the expect cost per unit time. Sensitivity analysis on the quantity of initial samples to estimate for in-control process mean vector and covariance matrix is also presented.