scholarly journals Economic Design of Variable Sampling Interval X Control Chart Using a Surrogate Variable

2013 ◽  
Vol 39 (5) ◽  
pp. 422-428 ◽  
Author(s):  
Tae-Hoon Lee ◽  
Jooho Lee ◽  
Minkoo Lee
Keyword(s):  
Control Chart ◽  
Sampling Interval ◽  
Economic Design ◽  
Variable Sampling Interval ◽  
Surrogate Variable ◽  
Variable Sampling

scholarly journals Economic Statistical Design of Variable Sampling Interval X¯ $\overline X $ Control Chart Based on Surrogate Variable Using Genetic Algorithms

2016 ◽  
Vol 7 (4) ◽  
pp. 54-64
Author(s):  
Tae-Hoon Lee ◽  
Sung-Hoon Hong ◽  
Hyuck-Moo Kwon ◽  
Minkoo Lee
Keyword(s):  
Genetic Algorithms ◽  
Control Chart ◽  
Statistical Design ◽  
Sampling Interval ◽  
Performance Variable ◽  
Variable Sampling Interval ◽  
Surrogate Variable ◽  
Variable Sampling ◽  
Proposed Model ◽  
A Performance

Abstract In many cases, a $\overline X $ control chart based on a performance variable is used in industrial fields. Typically, the control chart monitors the measurements of a performance variable itself. However, if the performance variable is too costly or impossible to measure, and a less expensive surrogate variable is available, the process may be more efficiently controlled using surrogate variables. In this paper, we present a model for the economic statistical design of a VSI (Variable Sampling Interval) $\overline X $ control chart using a surrogate variable that is linearly correlated with the performance variable. We derive the total average profit model from an economic viewpoint and apply the model to a Very High Temperature Reactor (VHTR) nuclear fuel measurement system and derive the optimal result using genetic algorithms. Compared with the control chart based on a performance variable, the proposed model gives a larger expected net income per unit of time in the long-run if the correlation between the performance variable and the surrogate variable is relatively high. The proposed model was confined to the sample mean control chart under the assumption that a single assignable cause occurs according to the Poisson process. However, the model may also be extended to other types of control charts using a single or multiple assignable cause assumptions such as VSS (Variable Sample Size) $\overline X $ control chart, EWMA, CUSUM charts and so on.

Design of a Variable Sampling Interval EWMA Median Control Chart

2019 ◽  
Vol 26 (05) ◽  
pp. 1950021 ◽  
Author(s):  
Kim Phuc Tran ◽  
Philippe Castagliola ◽  
Thi Hien Nguyen ◽  
Anne Cuzol
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Food Production ◽  
Average Run Length ◽  
Moving Average ◽  
Sampling Interval ◽  
Performance Comparison ◽  
Variable Sampling Interval ◽  
Variable Sampling ◽  
Weighted Moving Average

In the literature, median type control charts have been widely investigated as easy and efficient means to monitor the process mean when observations are from a normal distribution. In this work, a Variable Sampling Interval (VSI) Exponentially Weighted Moving Average (EWMA) median control chart is proposed and studied. The Markov chains are used to calculate the average run length to signal (ARL). A performance comparison with the original EWMA median control chart is made. The numerical results show that the proposed chart is considerably more effective as it is faster in detecting process shifts. Finally, the implementation of the proposed chart is illustrated with an example in food production process.

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