Optimization Online


Robust Optimization under Multi-band Uncertainty - Part I: Theory

Christina Büsing (buesing***at***or.rwth-aachen.de)
Fabio D'Andreagiovanni (d.andreagiovanni***at***zib.de)

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.

Keywords: Robust Optimization, Multi-band Uncertainty, Compact Robust Counterpart, Cutting Planes, Probabilistic Bound

Category 1: Robust Optimization

Category 2: Integer Programming ((Mixed) Integer Linear Programming )

Category 3: Combinatorial Optimization (Polyhedra )

Citation: Submitted (2012)

Download: [PDF]

Entry Submitted: 01/31/2013
Entry Accepted: 01/31/2013
Entry Last Modified: 03/14/2013

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society