@misc{BauschertBuesingD'Andreagiovannietal., author = {Bauschert, Thomas and B{\"u}sing, Christina and D'Andreagiovanni, Fabio and Koster, Arie M.C.A. and Kutschka, Manuel and Steglich, Uwe}, title = {Network Planning under Demand Uncertainty with Robust Optimization}, issn = {1438-0064}, doi = {10.1109/MCOM.2014.6736760}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42557}, abstract = {The planning of a communication network is inevitably depending on the quality of both the planning tool and the demand forecast used. In this article, we show exemplarily how the emerging area of Robust Optimization can advance the network planning by a more accurate mathematical description of the demand uncertainty. After a general introduction of the concept and its application to a basic network design problem, we present two applications: multi-layer and mixed-line-rate network design. We conclude with a discussion of extensions of the robustness concept to increase the accuracy of handling uncertainties.}, language = {en} } @article{BauschertBuesingD'Andreagiovannietal., author = {Bauschert, Thomas and B{\"u}sing, Christina and D'Andreagiovanni, Fabio and Koster, Arie M.C.A. and Kutschka, Manuel and Steglich, Uwe}, title = {Network planning under demand uncertainty with robust optimization}, series = {IEEE Communications Magazine}, volume = {52}, journal = {IEEE Communications Magazine}, number = {2}, doi = {10.1109/MCOM.2014.6736760}, pages = {178 -- 185}, language = {en} } @misc{BuesingD'Andreagiovanni, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio}, title = {A new theoretical framework for Robust Optimization under multi-band uncertainty}, issn = {1438-0064}, doi = {10.1007/978-3-319-00795-3_17}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42644}, abstract = {We provide an overview of our main results about studying Linear Programming Problems whose coefficient matrix is subject to uncertainty and the uncertainty is modeled through a multi-band set. Such an uncertainty set generalizes the classical one proposed by Bertsimas and Sim and is particularly suitable in the common case of arbitrary non-symmetric distributions of the parameters. Our investigations were inspired by practical needs of our industrial partner in ongoing projects with focus on the design of robust telecommunications networks.}, language = {en} } @misc{BuesingD'Andreagiovanni, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio}, title = {Robust Optimization under Multiband Uncertainty}, series = {2013 Mixed Integer Programming Workshop - MIP 2013}, journal = {2013 Mixed Integer Programming Workshop - MIP 2013}, language = {en} } @inproceedings{BuesingD'Andreagiovanni, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio}, title = {A New Theoretical Framework for Robust Optimization Under Multi-Band Uncertainty}, series = {Operations Research Proceedings 2012}, booktitle = {Operations Research Proceedings 2012}, doi = {10.1007/978-3-319-00795-3_17}, pages = {115 -- 121}, language = {en} } @misc{BuesingD'Andreagiovanni, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio}, title = {Robust Optimization under Multi-band Uncertainty - Part I: Theory}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-43469}, abstract = {The classical single-band uncertainty model introduced by Bertsimas and Sim has represented a breakthrough in the development of tractable robust counterparts of Linear Programs. However, adopting a single deviation band may be too limitative in practice: in many real-world problems, observed deviations indeed present asymmetric distributions over asymmetric ranges, so that getting a higher modeling resolution by partitioning the band into multiple sub-bands is advisable. The critical aim of our work is to close the knowledge gap on the adoption of multi-band uncertainty in Robust Optimization: a general definition and intensive theoretical study of a multi-band model are actually still missing. Our new developments have been also strongly inspired and encouraged by our industrial partners, interested in getting a better modeling of arbitrary shaped distributions, built on historical data about the uncertainty affecting the considered real-world problems.}, language = {en} } @inproceedings{BuesingD'AndreagiovanniRaymond, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio and Raymond, Annie}, title = {Robust optimization under multiband uncertainty}, series = {CTW 2013}, booktitle = {CTW 2013}, pages = {35 -- 38}, language = {en} } @inproceedings{BuesingD'AndreagiovanniRaymond, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio and Raymond, Annie}, title = {0-1 Multiband Robust Optimization}, series = {Operations Research Proceedings 2013}, booktitle = {Operations Research Proceedings 2013}, language = {en} } @misc{BuesingD'AndreagiovanniRaymond, author = {B{\"u}sing, Christina and D'Andreagiovanni, Fabio and Raymond, Annie}, title = {0-1 Multiband Robust Optimization}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-44093}, abstract = {We provide an overview of new theoretical results that we obtained while further investigating multiband robust optimization, a new model for robust optimization that we recently proposed to tackle uncertainty in mixed-integer linear programming. This new model extends and refines the classical Gamma-robustness model of Bertsimas and Sim and is particularly useful in the common case of arbitrary asymmetric distributions of the uncertainty. Here, we focus on uncertain 0-1 programs and we analyze their robust counterparts when the uncertainty is represented through a multiband set. Our investigations were inspired by the needs of our industrial partners in the research project ROBUKOM.}, language = {en} }