A methodology for stochastic optimization of multiple response simulation models

Simulation has become one of the most popular tools for the design and analysis of complex systems. The popularity of simulation is due to its flexibility, its ability to model systems more accurately than analytical models, and its ability to model the time dynamic behavior of systems. A major drawback of simulation, however, is that simulation is only a descriptive tool; therefore, in order to optimize a simulation model, it must be used in conjunction with an optimum seeking method. This paper describes an interactive (decision maker-computer) methodology for stochastic optimization of simulation models with multiple responses. The appraoch is illustrated with an example involving the optimization of an inventory policy.