Multiband Robust Optimization and its Adoption in Harvest Scheduling

Please always quote using this 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.
Author:Fabio D'Andreagiovanni, Annie Raymond
Document Type:ZIB-Report
Tag:Data Uncertainty; Harvest Scheduling; Multiband Robustness; Robust Optimization
MSC-Classification:68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)
Date of first Publication:2013/12/18
Series (Serial Number):ZIB-Report (13-76)
Licence (German):License LogoCreative Commons - Namensnennung

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