English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

"The Price of Robustness" by Bertsimas and Sim represented a breakthrough inthe development of a tractable robust counterpart of Linear ProgrammingProblems. However, the central modeling assumption that the deviation band ofeach uncertain parameter is single may be too limitative in practice:experience indeed suggests that the deviations distribute also internally tothe single band, so that getting a higher resolution by partitioning the bandinto multiple sub-bands seems advisable. The critical aim of our work is toclose the knowledge gap about the adoption of a multi-band uncertainty set inRobust Optimization: a general definition and intensive theoretical study of amulti-band model are actually still missing. Our new developments have beenalso strongly inspired and encouraged by our industrial partners, which havebeen interested in getting a better modeling of arbitrary distributions, builton historical data of the uncertainty affecting the considered real-worldproblems. In this paper, we study the robust counterpart of a LinearProgramming Problem with uncertain coefficient matrix, when a multi-banduncertainty set is considered. We first show that the robust counterpartcorresponds to a compact LP formulation. Then we investigate the problem ofseparating cuts imposing robustness and we show that the separation can beefficiently operated by solving a min-cost flow problem. Finally, we test theperformance of our new approach to Robust Optimization on realistic instancesof a Wireless Network Design Problem subject to uncertainty.

New results about multi-band uncertainty in Robust Optimization