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

Please always quote using this URN: urn:nbn:de:0297-zib-43469
  • 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.

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  • Submitted for publication in 2012. Preprint also available in Optimization Online (13-01-3748) and arXiv (1301.2734)

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Author:Christina Büsing, Fabio D'Andreagiovanni
Document Type:ZIB-Report
Tag:Robust Optimization, Multiband Uncertainty, Compact Robust Counterpart, Cutting Planes, Probabilistic Bound
Date of first Publication:2013/02/27
Series (Serial Number):ZIB-Report (13-10)
ISSN:1438-0064
Notes:
Submitted for publication in 2012. Preprint also available in Optimization Online (13-01-3748) and arXiv (1301.2734)
Licence (German):License LogoCreative Commons - Namensnennung
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