CORMSIS Seminar - Multilevel and Multistage Optimization, Professor Ted Ralphs (Lehigh) Seminar
For more information regarding this seminar, please email Yuan Huang at yuan.huang@southampton.ac.uk .
Event details
Abstract: Traditional mathematical optimization models are premised on the assumption that there is a single decision to be made at a fixed point in time with a fixed objective and that the decision-maker (DM) has deterministic knowledge of all problem inputs. Multilevel/multistage optimization is a generalized framework that allows for multiple (possibly competing) DMs acting at multiple points in time. The framework subsumes both game theoretic models, in which multiple DMs with competing objectives make decisions sequentially, and recourse models, in which a single DM must make a sequence of decisions over time in order to react to changing conditions. In this talk, we'll discuss the basic concepts underlying this approach to modelling real-world optimization problems, focusing particularly on the challenging case in which the models involve discrete decisions. We'll first discuss the modelling framework itself, what types of applications it's appropriate for, and what makes solution of these problems inherently challenging. Finally, we'll discuss practical solution algorithms.
Speaker information
Professor Ted Ralphs , About the speaker: Dr Ted Ralphs received his Ph.D. in Operations Research from Cornell University in 1995. He is currently a Professor in the Department of Industrial and Systems Engineering (ISE) at Lehigh University, where he is Director of the Laboratory for Computational Optimization Research at Lehigh (COR@L). He is a co-founder of the COIN-OR Foundation, a non-profit foundation promoting the development of open source software for Operations Research and is currently Chair of the Technical Leadership Council and a member of the Strategic Leadership Board, as well as project manager of a number of projects hosted in the COIN-OR open source software repository. His research interests include development of methodology for solving discrete optimization problems, including those with multiple levels or multiple objectives; development of parallel search algorithms; development of open source software; and applications of discrete optimization.