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Seminars

Risk-Averse Stochastic Programming

Date: 04-10-2007
Start Time: 1:00pm
End Time: 2:00pm
Speaker: Shabbir Ahmed, Georgia Institute of Technology
Location: Mudd 303

ABSTRACT

Traditional stochastic programming is risk-neutral in the sense that it is concerned with the optimization of an expectation criterion. A common approach for addressing risk is to consider a weighted mean-risk objective, where some dispersion statistic is used as a measure of risk. We explore, from an algorithmic perspective, the suitability of various mean-risk objective functions in addressing risk aversion in stochastic programming models. A common approach for solving standard stochastic programs is the sample average approximation (SAA) method. In this approach, the expected value objective of the problem is approximated by a sample average objective, and the resulting approximating problem is solved using deterministic optimization methods. We extend the SAA approach for solving some classes of risk-averse stochastic programming problems. First, we investigate theoretical convergence properties of an optimal solution of the sample average approximating problem to that of the true risk-averse stochastic program. Next, we develop efficient decomposition methods for solving sample average approximations of some classes of risk-averse stochastic programs. We also discuss statistical bounding techniques to validate the quality of a solution obtained by the proposed SAA approach.

BIO

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. His research interests are in theoretical and algorithmic aspects of discrete and stochastic optimization problems. He serves on the international Committee on Stochastic Programming, as vice-chair of stochastic programming in the INFORMS Optimization Society, and 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.