A HYBRID MODEL BASED ON STOCHASTIC GOAL PROGRAMMING AND RESPONSE SURFACE METHODOLOGY TO OPTIMIZE

Nowadays, quality is known as a commercial strategy to increase the market share and as it comes off, it will end up causing important problems such as customers' dissatisfaction leading to market share reduction and finally elimination from the world of competition. Therefore, the topics related to quality engineering has a vital importance in the industry. Several methods have been proposed in this regard, one of which is the response surface methodology. When the relation among the variables of a process are not clear, and the experimenter is interested in finding the optimal adjustments of the input variables and that's why the response surface methodology is utilized to optimize the process parameters. In this study, the response surface methodology has been exploited with the robust designing approach in order to optimize the quality characteristics of the product and effective variables so that in the beginning the control variables and response variables must be identified and then the model should be optimized by designing the experiment and finding the intended regression equations.