Multipolar robust optimization
Walid Ben-Ameur , Adam Ouorou , Guanglei Wang , Mateusz Żotkiewicz
AbstractWe consider linear programs involving uncertain parameters and propose a new tractable robust counterpart which contains and generalizes several other models including the existing Affinely Adjustable Robust Counterpart and the Fully Adjustable Robust Counterpart. It consists in selecting a set of poles whose convex hull contains some projection of the uncertainty set, and computing a recourse strategy for each data scenario as a convex combination of some optimized recourses (one for each pole). We show that the proposed multipolar robust counterpart is tractable and its complexity is controllable. Further, we show that under some mild assumptions, two sequences of upper and lower bounds converge to the optimal value of the fully adjustable robust counterpart. We numerically investigate a couple of applications in the literature demonstrating that the approach can effectively improve the affinely adjustable policy.
|Journal series||EURO Journal on Computational Optimization, ISSN 2192-4406, [2192-4414], (0 pkt)|
|Publication size in sheets||1.95|
|Keywords in English||Uncertainty, Robust optimization, Multistage optimization, Polyhedral approximation|
|Score||= 5.0, 20-10-2019, ArticleFromJournal|
|Publication indicators||= 3|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.