Abstract
The objective of this paper is to demonstrate how a combination of design optimization theory and methodology can be applied to large-scaled industrial systems to efficiently improve their performance, reduce their costs or improve other design objectives. The scheme described was developed when other conventional design and optimization strategies failed in efficiently optimizing an air-to-air missile design for Lockheed Martin Missile and Fire Control. The efficient design scheme was developed for the system using a combination of optimization and design of experiment techniques. It will be shown that multidisciplinary design optimization techniques can be improved with a dependency-tracking demand-driven language resulting in an attractive choice for solving industrial type design problems. The design methodology holds true even for systems in which a large number of disciplinary design and analysis software are integrated. One reason for the efficiency of the scheme is the parameterized dependency-tracking environment in which the optimizations are carried out. With the hybrid approach developed, combining exploration and optimization techniques with the unique dependency-tracking and demand driven features of the environment, it was possible to reduce the computational time by as much as 44%. The design scheme developed and presented can be used to improve the design and optimization process for numerous other engineering applications.