Optimization design of control charts based on minimax decision criterion and fuzzy process shifts

A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts

“…They also use genetic algorithms to find the best design parameters with which the control chart has to be designed. Chen et al [5] present a procedure for optimally designing the control chart when there is vagueness in the process shift. They treat the process shift as a fuzzy number with a given membership function, and take a range of values from the fuzzy a-cut sets, representing possible shifts, to implement the chart design.…”

A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts

“…They also used genetic algorithms to find the best design parameters with which the control chart had to be designed. Chen et al [5] presented a procedure for optimally designing the control chart with vagueness in the process shift. They treated the process shift as a fuzzy number with a given membership function, and takes a range of values from the fuzzy cut sets for possible shifts to implement the chart design.…”

A hybrid fuzzy adaptive sampling – Run rules for Shewhart control charts

“…They also used genetic algorithms to find the best design parameters with which the control chart had to be designed. Chen et al [6] presented a procedure for optimally designing the control chart with vagueness in the process shift. They treated the process shift as a fuzzy number with a given membership function, and takes a range of values from the fuzzy cut sets for possible shifts to implement the chart design.…”