odpbc_binom: Optimal design parameters of the Percentile-based attribute...

In pbcc: Percentile-Based Control Chart

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: [e-mail]

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)

pbcc documentation built on April 3, 2025, 10:06 p.m.