F
Fabio D'Andreagiovanni
Researcher at Centre national de la recherche scientifique
Publications - 81
Citations - 1155
Fabio D'Andreagiovanni is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Robust optimization & Wireless network. The author has an hindex of 18, co-authored 78 publications receiving 961 citations. Previous affiliations of Fabio D'Andreagiovanni include University of Technology of Compiègne & Zuse Institute Berlin.
Papers
More filters
Book ChapterDOI
New results about multi-band uncertainty in Robust Optimization
TL;DR: This work investigates the problem of separating cuts imposing robustness and shows that the robust counterpart corresponds to a compact LP formulation, and tests the performance of the new approach to Robust Optimization on realistic instances of a Wireless Network Design Problem subject to uncertainty.
Journal ArticleDOI
Network planning under demand uncertainty with robust optimization
Thomas Bauschert,Christina Büsing,Fabio D'Andreagiovanni,Arie M. C. A. Koster,Manuel Kutschka,Uwe Steglich +5 more
TL;DR: This article shows by example how the emerging area of robust optimization can advance the network planning by a more accurate mathematical description of the demand uncertainty by presenting two applications: multi-layer and mixed-line-rate network design.
Journal ArticleDOI
On the energy cost of robustness for green virtual network function placement in 5G virtualized infrastructures
TL;DR: This paper proposes novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs, and proposes both exact and heuristic methods.
Journal ArticleDOI
Towards the fast and robust optimal design of wireless body area networks
TL;DR: This work proposes the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty, and proposes an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search.
Journal ArticleDOI
A fast hybrid primal heuristic for multiband robust capacitated network design with multiple time periods
TL;DR: A hybrid primal heuristic that combines a randomized fixing strategy inspired by ant colony optimization and an exact large neighbourhood search is proposed that can run fast and produce solutions of extremely high quality associated with low optimality gaps.