Please use this identifier to cite or link to this item:
Title: Distributionally robust optimization and its tractable approximations
Authors: Goh, J. 
Sim, M. 
Keywords: Programming
Issue Date: 2010
Citation: Goh, J., Sim, M. (2010). Distributionally robust optimization and its tractable approximations. Operations Research 58 (4 PART 1) : 902-917. ScholarBank@NUS Repository.
Abstract: In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible. Next, we develop new piecewise-linear decision rules that allow a more flexible reformulation of the original problem. The reformulated problem will generally contain terms with expectations on the positive parts of the recourse variables. Finally, we convert the uncertain linear program into a deterministic convex program by constructing distributionally robust bounds on these expectations. These bounds are constructed by first using different pieces of information on the distribution of the underlying uncertainties to develop separate bounds and next integrating them into a combined bound that is better than each of the individual bounds. © 2010 INFORMS.
Source Title: Operations Research
ISSN: 0030364X
DOI: 10.1287/opre.1090.0795
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jun 7, 2023


checked on Jun 7, 2023

Page view(s)

checked on May 25, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.