Abstract

Peter Fleming is Chair of Industrial Systems and Control in the Department of Automatic Control and Systems Engineering and Director of the Rolls-Royce University Technology Centre for Control and Systems Engineering at the University of Sheffield, UK. His control and systems engineering research interests include control system design, health monitoring, multi-criteria decision-making, optimisation and scheduling, and applications of e-Science. Further details may be found at http://www.shef.ac.uk/acse/staff/peter_fleming

Abstract: Design problems arising in control and systems can often be conveniently formulated as multi-criteria decision-making problems. However, these often comprise a relatively large number of criteria. Through close association with designers in industry a range of machine learning tools and associated techniques have been devised to address the special requirements of many-criteria decision-making. These include visualisation and analysis tools to aid the identification of features such as “hot-spots” and non-competing criteria, preference articulation techniques to assist in interrogating the search region of interest and methods to address the special computational demands of these problems. Test problems and real design exercises will demonstrate these approaches.