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Adjustable Robust Optimization with Discrete Uncertainty.

Authors :
Lefebvre, Henri
Malaguti, Enrico
Monaci, Michele
Source :
INFORMS Journal on Computing; Jan/Feb2024, Vol. 36 Issue 1, p78-96, 19p
Publication Year :
2024

Abstract

In this paper, we study adjustable robust optimization (ARO) problems with discrete uncertainty. Under a very general modeling framework, we show that such two-stage robust problems can be exactly reformulated as ARO problems with objective uncertainty only. This reformulation is valid with and without the fixed recourse assumption and is not limited to continuous wait-and-see decision variables unlike most of the existing literature. Additionally, we extend an enumerative algorithm akin to a branch-and-cut scheme for which we study the asymptotic convergence. We discuss how to apply the reformulation on two variants of well-known optimization problems, a facility location problem in which uncertainty may affect the capacity values and a multiple knapsack problem with uncertain weights, and we report extensive computational results demonstrating the effectiveness of the approach. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms – Discrete. Funding: This work was supported by the Air Force Office of Scientific Research [Grants FA8655-20-1-7012, FA8655-20-1-7019]. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10919856
Volume :
36
Issue :
1
Database :
Complementary Index
Journal :
INFORMS Journal on Computing
Publication Type :
Academic Journal
Accession number :
175282550
Full Text :
https://doi.org/10.1287/ijoc.2022.0086