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Pareto Efficiency in Robust Optimization

In this talk, we formalize and adapt the well-known concept of Pareto efficiency in the context of the popular robust optimization (RO) methodology. We argue that the classical RO paradigm need not produce solutions that possess the associated property of Pareto optimality, and illustrate via examples how this could lead to inefficiencies and sub-optimal performance in practice. We provide a basic theoretical characterization of Pareto robustly optimal (PRO) solutions, and extend the RO framework by proposing practical methods that verify Pareto optimality, and generate solutions that are PRO. Critically important, our methodology involves solving optimization problems that are of the same complexity as the underlying robust problems, hence the potential improvements from our framework come at essentially no computational cost. We perform numerical experiments drawn from three different application areas (portfolio optimization, inventory management, and project management), which demonstrate that PRO solutions have a significant upside compared with solutions obtained via classical RO methods, at no extra cost or downside. ( Joint work with Nikolaos Trichakis from HBS )

Type of Seminar:
Optimization and Applications Seminar
Prof. Dan Iancu
Department of Operations, Information and Technology, Stanford University’s Graduate School of Business
Mar 18, 2013   4:30 p.m.

HG G 19.2, Rämistrasse 101
Contact Person:

John Lygeros
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Biographical Sketch:
Dan Iancu is an Assistant Professor of Operations, Information and Technology at Stanford University’s Graduate School of Business. Prior to joining the faculty at the Graduate School of Business, he spent one year as a Goldstine Fellow in the Risk Analytics Group at the IBM T.J. Watson Research Center. A native of Romania, Professor Iancu holds a BS degree in Electrical Engineering and Computer Science from Yale University (2004), an SM in Engineering Sciences from Harvard University (2006), and a PhD in Operations Research from the Massachusetts Institute of Technology (2010). He was the recipient of the Best Student Paper Prize of the INFORMS Optimization Society (2009), and of two teaching prizes, at Harvard and at MIT Sloan.