Experience in combining AI and optimization in different engineering domains

Abstract: "The course Synthesis of Engineering Systems has been offered by the Engineering Design Research Center over the last three years.In this interdisciplinary course students are exposed to two major paradigms for design synthesis: mathematical programming and knowledge based sytems. The former emphasizes the mathematical formulation of optimization problems that involve discrete and continuous variables forthe selection of topologies and parameters in engineering systems. The latter emphasizes representations and search techniques for processing qualitative knowledge for synthesis of designs by hierarchical decomposition. In the fall of 1990 the two authors taught this course andassigned a term project in which the students applied both synthesis approaches to a design problem in their domain.Most of the projects dealt with civil, chemical and mechanical engineering problems, reflecting the disciplinary background of the students.Overall the quality of these projects was very good and they showed a number of different schemes for combining AI and optimization. For instance, some used AI techniques for preliminary screening or as critics of mathematical optimization, while others showed a direct comparisonbetween the approaches. This paper summarizes the experience gained in the course, illustrates representative student projects, discusses the major results and conclusions, and provides a perspective for future research needs and educational approaches in this area."