A new robust design optimization model using Taguchi loss function

Quality loss function is common techniques in robust design terminology that consider the deviation of output from ideal point and variability as well. Mostly in practice, processes are affected by external uncontrollable factors that causes output of process to be far from ideal points with variability around its exact value. In this research, the common Taguchi quality loss function is applied to propose a new robust optimization model that able to choice optimal results of input variables. In this model, the quality loss function is expanded and a nonlinear optimization model is introduced in order to minimize the effect of environmental noise variables. At the end, a numerical example is presented to show the applicability of the proposed model for investigating the best levels of input variables in noisy process.