Multi-response simulation optimization using stochastic genetic search within a goal programming framework

F.F. Baesler, J.A. Sepulveda
2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165)  
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
more » ... ally, 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.
doi:10.1109/wsc.2000.899865 dblp:conf/wsc/BaeslerS00 fatcat:bmajsmbcdre6rcapzrxiqmyvfq