Uncertainty reduction in robust optimization
ARSLAN, Ayşe
Formulations étendues et méthodes de décomposition pour des problèmes génériques d'optimisation [EDGE]
Formulations étendues et méthodes de décomposition pour des problèmes génériques d'optimisation [EDGE]
ARSLAN, Ayşe
Formulations étendues et méthodes de décomposition pour des problèmes génériques d'optimisation [EDGE]
< Reduce
Formulations étendues et méthodes de décomposition pour des problèmes génériques d'optimisation [EDGE]
Language
en
Document de travail - Pré-publication
This item was published in
2023-07-11
English Abstract
Uncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decisiondependent uncertainty. Herein, we first show that when the uncertainty reduction decisions are ...Read more >
Uncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decisiondependent uncertainty. Herein, we first show that when the uncertainty reduction decisions are constrained, the resulting optimizationproblem is NP-hard. We further show that relaxing these constraints leads to solving a linear number of deterministic problems in certain special cases and illustrate the numerical relevance of this result. We further provide insights into possible MILP reformulations and the strength of their continuous relaxations.Read less <
English Keywords
Combinatorial optimization
Robust optimization
NP-hardness
Reformulation
ANR Project
Bornes primales et duales pour optimisation robuste adjustable - ANR-22-CE48-0018
Origin
Hal imported