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Title : Distribution Robustness in Stochastic Programming
Date : February 25, 2008
Speaker : Shabbir Ahmed, Coca Cola Associate Professor
Affiliation : H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Abstract
A key criticism of classical stochastic programming is the need for accurate distribution information for the uncertain problem parameters. We discuss some stochastic programming models that are in some sense immunized with respect to distribution inaccuracies. From a dual perspective, such models also explicitly address risk-aversion. We generalize algorithmic techniques in classical stochastic programming to solve such distribution-robust models. Finally, we discuss applications of such models in finance and supply chain.
Biosketch
Shabbir Ahmed is a Coca-Cola Associate Professor in the H. Milton Stewart School of Industrial & Systems Engineering at the Georgia Institute of Technology. He holds a PhD in Industrial Engineering from the University of Illinois at Urbana-Champaign. Dr. Ahmed's research interests are in theory and applications of discrete and stochastic optimization.
He is the chair of the international Committee on Stochastic Programming, vice-chair of stochastic programming in the INFORMS Optimization Society, and serves on the editorial boards of several journals. His honors include the National Science Foundation CAREER award, two IBM Faculty Awards, and the INFORMS Dantzig Dissertation award.
