Constrained sequential-block search in simulation experimentation

This paper describes the application of sequential-block search techniques to simulation experimentation with constrained systems. Two basically different approaches are examined. One approach combines designed experiments, multiple regression, and mathematical optimization to predict a constrained optimum solution, which is then checked by further experimentation in the region of the predicted solution. A second approach employs a sequential optimum seeking-technique, such as gradient search or sequential simplex search, modified to accommodate constraints. These techniques are illustrated with a simple inventory system modeled with the GASP-II simulation language. A comparison of the effectiveness of these approaches is presented.