Authors
Abstract
One of the applicable techniques which are used for modeling and solving optimization problems is response surface methodology (RSM). Response surface methodology is a collection of tools for fitting a surface to a set of data and determines optimal levels, this method used of a regression model for optimization problems Some real world problems include determining optimum values of input variables in order to obtain the desired levels of output variable (response surface variable). In this paper considering the Importance of four factors: Cooling Time, Pressure of injection, injection Speed and Heater temperature as the independent input variables for the variables of effective input factors and the response surface quantity and qualitative variable (Twin and Concurrently) in relation between input variables and response surface variables with the nonlinear regression model. Response variables would be crisp and fuzzy, for this Reason has been used of bi-level programming and fuzzy dual response surface methodology. Then the optimal value of each parameter is obtained by Meta-Heuristics algorithm (NSGA-II).
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