Potential applications will be investigated of artificial intelligence and expert systems to the design and operation of materials logistics systems within semiconductor manufacturing. The United States semiconductor industry faces considerable pressure from international competition in a time of rapidly changing products and processes. Problems such as scheduling and dispatching within a production line are computationally intractible and must rely on heuristics for system operation. Their complexity, size, and stochastic variability suggest that they are best addressed by a combination of techniques from Operations Research and Artificial Intelligence. Dr. Shilepsky will bring together current models from Operations Research, scaling to reduce problem size, and embedded AI submodules in the form of expert systems, neural nets, and/or space state search to develop new models for production planning. The interactive activities include teaching two courses during each of the fall and spring semesters selected from Introduction to Engineering Probability, Introduction to Stochastic Processes, and Inventory Theory. In addition, Dr. Shilepsky will advise 10 undergraduate and 5 graduate students, supervising a master's degree project in conjunction with the proposed research. This project furthers VPW program objectives which are (1) to provide opportunities for women to advance their careers in engineering and in the disciplines of science supported by NSF and (2) to encourage women to pursue careers in science and engineering by providing greater visibility for women scientists and engineers employed in industry, government, and academic institutions. By encouraging the participation of women in science, it is a valuable investment in the Nation's future scientific vitality.
