Multi-Response Simulation Optimization Using Genetic Algorithm Within Desirability Function Framework

This paper presents a new methodology to solve multi-response statistical optimization problems. This methodology integrates desirability function and simulation approach with a genetic algorithm. The desirability function is responsible for modeling the multi-response statistical problem, the simulation approach generates required input data from a simulated system, and finally the genetic algorithm tries to optimize the model. This methodology includes two methods. The methods differ from each other in controlling the randomness of the problem. In the first method, replications control this randomness and, while in the second method we control the variation by statistical tests.

Keywords:

Multi-Response , Genetic Algorithm , Desirability Function , Simulation