Simulation optimization: multi-response simulation optimization using stochastic genetic search within a goal programming framework

TLDR: In this paper, a new approach to solve multi-response simulation optimization problems is presented, which integrates a simulation model with a genetic algorithm heuristic and a goal programming model, and the search is performed stochastically and not deterministically like most of the approaches reported in the literature.

Abstract: This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.