Multipolar robust optimization

We 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 p...

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Autores principales:	Walid Ben-Ameur, Adam Ouorou, Guanglei Wang, Mateusz Żotkiewicz
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2018
Materias:	90C99 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/db2a8344ac1b40e4ab2437a593d34689
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spelling	oai:doaj.org-article:db2a8344ac1b40e4ab2437a593d346892021-12-02T05:01:09ZMultipolar robust optimization2192-440610.1007/s13675-017-0092-4https://doaj.org/article/db2a8344ac1b40e4ab2437a593d346892018-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621001088https://doaj.org/toc/2192-4406We 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.Walid Ben-AmeurAdam OuorouGuanglei WangMateusz ŻotkiewiczElsevierarticle90C99Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 6, Iss 4, Pp 395-434 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	90C99 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90C99 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 Walid Ben-Ameur Adam Ouorou Guanglei Wang Mateusz Żotkiewicz Multipolar robust optimization
description	We 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.
format	article
author	Walid Ben-Ameur Adam Ouorou Guanglei Wang Mateusz Żotkiewicz
author_facet	Walid Ben-Ameur Adam Ouorou Guanglei Wang Mateusz Żotkiewicz
author_sort	Walid Ben-Ameur
title	Multipolar robust optimization
title_short	Multipolar robust optimization
title_full	Multipolar robust optimization
title_fullStr	Multipolar robust optimization
title_full_unstemmed	Multipolar robust optimization
title_sort	multipolar robust optimization
publisher	Elsevier
publishDate	2018
url	https://doaj.org/article/db2a8344ac1b40e4ab2437a593d34689
work_keys_str_mv	AT walidbenameur multipolarrobustoptimization AT adamouorou multipolarrobustoptimization AT guangleiwang multipolarrobustoptimization AT mateuszzotkiewicz multipolarrobustoptimization
_version_	1718400859249836032

Multipolar robust optimization

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