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Multiband Robust Optimization and its Adoption in Harvest Scheduling

Zitieren Sie bitte immer diese URN: urn:nbn:de:0297-zib-43380
  • A central assumption in classical optimization is that all the input data of a problem are exact. However, in many real-world problems, the input data are subject to uncertainty. In such situations, neglecting uncertainty may lead to nominally optimal solutions that are actually suboptimal or even infeasible. Robust optimization offers a remedy for optimization under uncertainty by considering only the subset of solutions protected against the data deviations. In this paper, we provide an overview of the main theoretical results of multiband robustness, a new robust optimization model that extends and refines the classical theory introduced by Bertsimas and Sim. After introducing some new results for the special case of pure binary programs, we focus on the harvest scheduling problem and show how multiband robustness can be adopted to tackle the uncertainty affecting the volume of produced timber and grant a reduction in the price of robustness.
Metadaten
Verfasserangaben:Fabio D'Andreagiovanni, Annie Raymond
Dokumentart:ZIB-Report
Freies Schlagwort / Tag:Data Uncertainty; Harvest Scheduling; Multiband Robustness; Robust Optimization
MSC-Klassifikation:68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)
90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Datum der Erstveröffentlichung:18.12.2013
Schriftenreihe (Bandnummer):ZIB-Report (13-76)
ISSN:1438-0064
Lizenz (Deutsch):License LogoCreative Commons - Namensnennung
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