A new approach to solve multi-response statistical optimization problems using neural network, genetic algorithm, and goal attainment methods

Adjusting control factors (independent variables) to achieve an optimal level of output (response variable) is usually required in many real-world manufacturing problems. Common optimization methods often begin with estimating the relationship between a response and independent variables. Among these techniques, response surface methodology (RSM), due to its simplicity, has recently attracted extensive attention. However, on the one hand, in some cases, the relationship between a response and independent variables is too complex to be estimated using polynomial regression models. On the other hand, solving the obtained optimization model is not easy by exact methods. This paper introduces a new methodology to solve multi-response statistical optimization problems. The novel hybrid approach of this research involves a modeling technique, a neural network methodology, and a genetic algorithm. The modeling technique that is selected among three common available methods is responsible to model the multi-response statistical problem. The neural network approach generates required input data, and finally, the genetic algorithm tries to optimize the model and find the adjusted levels of the control factors. At the end, the application and the performance of the proposed methodology are demonstrated using numerical examples. The results of several statistical analyses show that the proposed methodology is superior to other available methods in the literature.