Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty

Michael Poss

Résumé

Given a nominal combinatorial optimization problem, we consider a robust two-stages variant with cost uncertainty, called DDID. In the first stage, DDID selects a subset of uncertain cost coefficients to be observed, and in the second-stage, DDID selects a solution to the nominal problem, where the remaining cost coefficients are still uncertain. We further assume that the uncertainty set is a polytope defined by upper bounds and a constant number of knapsack constraints. Given a compact linear programming formulation for the nominal problem, we provide a compact mixed-integer linear programming formulation for DDID. The formulation leads to polynomial-time algorithms for DDID when the number of possible observations is polynomially bounded. We extend this formulation to more general nominal problems through column generation and constraint generation algorithms. We illustrate our reformulations and algorithms numerically on the selection problem, the orienteering problem, and the spanning tree problem.
Fichier principal
Vignette du fichier
general_paper.pdf (486.39 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04097679 , version 1 (15-05-2023)
hal-04097679 , version 2 (05-09-2023)
hal-04097679 , version 3 (22-12-2023)

Licence

Paternité

Identifiants

  • HAL Id : hal-04097679 , version 1

Citer

Jérémy Omer, Michael Poss. Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty. 2023. ⟨hal-04097679v1⟩
254 Consultations
195 Téléchargements

Partager

Gmail Facebook X LinkedIn More