MULTIDISCIPLINARY ANALYSIS AND OPTIMIZATION OF DISCRETE PROBLEMS USING RESPONSE-SURFACE METHODS

The objective of this paper is to present a new algorithm to efficient ly optimize multidisciplinary coupled nonhierarchic systems with discr ete variables. The algorithm decomposes the system into contributing d isciplines, and uses designed experiments within the disciplines to bu ild local response surface approximations to the discipline analysis. First and second order Global Sensitivity Equations are formulated and approximated by experimental data To build approximations to the glob al design space. The global approximation is optimized using branch an d bound or simulated annealing. Convergence is rapid for systems with near quadratic behavior. The algorithm is demonstrated on a unique mul tidisciplinary learning tool, the Design and Manufacturing Learning En vironment. This environment provides multimedia simulation for product life cycle disciplines, including design, manufacturing, marketing, a nd sales.