A simulation based decision support system is developed for AT&T Microelectronics in Orlando. This system uses simulation modeling to capture the complex nature of semiconductor test operations. Simulation, however, is not a tool for optimization by itself. Numerous executions of the simulation model must generally be performed to narrow in on a set of proper decision parameters. As a means of alleviating this shortcoming, artificial neural networks are used in conjunction with simulation modeling to aid management in the decision making process. The integration of simulation and neural networks in a comprehensive decision support system, in effect, learns the reverse of the simulation process. That is, given a set of goals defined for performance measures, the decision support system suggests proper values for decision parameters to achieve those goals
