odpbc_binom: Optimal design parameters of the Percentile-based attribute...
| odpbc_binom | R Documentation |
Optimal design parameters of the Percentile-based attribute control charts
Description
Determine the optimal statistical design parmeters of percentile-based p-chart.
Usage
odpbc_binom(nmax, T1, T2, hv, mat, ip, sp, p1=0.05, p2=0.05, pop.size = 1000)
Arguments
nmax |
The maximum possible sample size in each sampling interval (a numeric value). |
T1 |
The desired in-control time to signal (a numeric value). |
T2 |
The desired out-of-control time to signal (a numeric value). |
hv |
The vector of intersample interval upto maximum T2 (a numeric vector of possible sample intervals). |
mat |
The matrix of minimum and maximum bounds for optimum parameters sample size and sample interval. The minimum values of n and h are (2,0.5) and maximum value are (nmax, T2).However minimun sample size should be selected according to recommended no |
ip |
In-control value of monitoring parameter (a numeric value between 0 and 1) |
sp |
Shifted-value of monitoring parameter (a numeric value between 0 and 1) |
p1 |
The probability to signal in-control from a specified number (default value is 5%) |
p2 |
The probability to signal out-of-control from a specified number (default value is 5%) |
pop.size |
Population size. This is the number of individuals genoud uses to solve the optimization problem for genetic algorithem (default value is 1000). |
Value
Returns the optimal parameters of PL type control chart:
n |
The optimal sample size to design the percentile-based p-chart. |
h |
The optimal intersampling interval to design the percentile-based p-chart. |
k |
The optimal control chart constant/multiplier to design the percentile p-chart |
Author(s)
Aamir Saghir, Khan Zahid, Zsolt T. Kosztyan*
e-mail: kosztyan.zsolt@gtk.uni-pannon.hu
References
Faraz A, Saniga E, Montgomery D. (2019). Percentile-based control charts design with an application to Shewhart Xbar and S2 control charts. Quality and Reliability Engineering International, 35(1); 116-126.
See Also
pbcc,summary.pbcc,plot.pbcc,odpbc.
Examples
# set maximum sample size
nmax=500
# Set the process in-control time to signal is at least 100 samples.
T1= 100
# Set the out-of-control chart time to signal is at most 1 sample
T2=3
# Set the sampling intersample intervals to 0.5(0.5) T2 units of time.
hv=seq(0.5, T2, by=0.5)
# Set the probability of guaranteed in-control signal 5%/10%/15%
p1=0.05
#Set the probability of guaranteed out-of-control signals 5%/10%/15%
p2=0.05
#Set the lower and upper bounds of parameters(n and h) used in the optimization.
# 81 minimum value is taken to hold the normality condition np>=5 or n(1-p)>=5
mat=matrix(c(2, nmax, 1, length(hv)), 2,2, byrow=TRUE)
# In-control value of monitoring parameter p
ip=0.10
# out of control value of monitoring parameter p
sp=0.15
output<-odpbc_binom(nmax, T1, T2, hv, mat, ip, sp, p1, p2, pop.size = 1000)
print(output)
