Economic statistical design of x¯ control charts for systems with Weibull in-control times
Computers and Industrial Engineering
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
Expert Systems with Applications: An International Journal
Economic design of variable sampling intervals T2 control charts using genetic algorithms
Expert Systems with Applications: An International Journal
Generating robust and flexible job shop schedules using genetic algorithms
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper presented an approach which simultaneously considered the properties of cost and quality based on the Burr distribution and the non-unifampling scheme. The objective was to determine three parameters, namely, sample size, sampling interval between successive samples, and control limits, when an X bar chart monitors a manufacturing process with Gamma (@l, 2) failure characteristic and non-normal data. The design parameters of the X bar control charts can be obtained through the genetic algorithm (GA) method. An example was also adopted to indicate the solution procedure and sensitivity analyses. The results show that an increase of skewness coefficient (@a"3) results in a slight decrease for sample size (n) while an increase of kurtosis coefficient (@a"4) leads to a wider control limit width.