Monitoring Process Variability: A Hybrid Taguchi Loss and Multiobjective Genetic Algorithm Approach

The common consideration on economic model is that there is knowledge about the risk of occurrence of an assignable cause and thevarious cost parameters that does not always adequately describe what happens in practice. Hence, there is a need for more realisticassumptions to be incorporated. In order to reduce cost penalties for not knowing the true values of some parameters, this paper aims todevelop a bi-objective model of the economic-statistical design of the S control chart to minimize the mean hourly loss cost whileminimizing out-of-control average run length and maintaining reasonable in-control average run length considering Taguchi loss function.The purpose of Taguchi loss function is to reflect the economic loss associated with variation in, and deviations from, the process target orthe target value of a product characteristic. In contrast to the existing modeling approaches, the proposed model and given Pareto-optimalsolution sets enables the chart designer to obtain solutions that is effective even for control chart design problems in uncertainenvironments. A comparison study with a traditional economic design model reveals that the proposed chart presents a better approach forquality system costs and the power of control chart in detecting the assignable cause.

Keywords:

Economic-Statistical design , Taguchi loss function , NSGA-II Algorithm , Process variability , Immeasurable costs