Please use this identifier to cite or link to this item:
|Title:||Models for minimax stochastic linear optimization problems with risk aversion||Authors:||Bertsimas, D.
|Keywords:||Minimax stochastic optimization
|Issue Date:||2010||Citation:||Bertsimas, D., Doan, X.V., Natarajan, K., Teo, C.-P. (2010). Models for minimax stochastic linear optimization problems with risk aversion. Mathematics of Operations Research 35 (3) : 580-602. ScholarBank@NUS Repository. https://doi.org/10.1287/moor.1100.0445||Abstract:||We propose a semidefinite optimization (SDP) model for the class of minimax two-stage stochastic linear optimization problems with risk aversion. The distribution of second-stage random variables belongs to a set of multivariate distributions with known first and second moments. For the minimax stochastic problem with random objective, we provide a tight SDP formulation. The problem with random right-hand side is NP-hard in general. In a special case, the problem can be solved in polynomial time. Explicit constructions of the worst-case distributions are provided. Applications in a productiontransportation problem and a single facility minimax distance problem are provided to demonstrate our approach. In our experiments, the performance of minimax solutions is close to that of data-driven solutions under the multivariate normal distribution and better under extremal distributions. The minimax solutions thus guarantee to hedge against these worst possible distributions and provide a natural distribution to stress test stochastic optimization problems under distributional ambiguity. Copyright © 2010 INFORMS.||Source Title:||Mathematics of Operations Research||URI:||http://scholarbank.nus.edu.sg/handle/10635/44018||ISSN:||0364765X||DOI:||10.1287/moor.1100.0445|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 16, 2019
WEB OF SCIENCETM
checked on Sep 6, 2019
checked on Sep 9, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.