Using Predictive Modeling Techniques to Solve Multilevel Systems Design Problems

摘要

A challenging aspect of systems design is the search for a combination of concepts for system components that together yield desirable system-level characteristics. These system-level characteristics depend not only on the concepts designers choose, but also the details of how they implement them. This yields a challenging search problem through a heterogeneous and discontinuous space of system alternatives. Although designers can use design optimization methods for this task, they can be slow because they entail solving a different optimization problem for every valid combination of component-level concepts. In this paper, we present a new approach to systems design based on the use of abstract predictive models. Under this approach, designers abstract multiple physically heterogeneous component-level concepts into a unified model that captures the salient characteristics of the possible implementations of each concept. This enables them to search the space of system alternatives quickly and effectively. We demonstrate the new approach on a utility cart system design problem and compare it to optimization-based approaches common in the literature. The new approach yields a system design as desirable as the one we obtain from the best-performing optimization-based approach, but in less than one-tenth the computational time.