Multiband Robust Optimization and its Adoption in Harvest Scheduling

Abstract

Abstract: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.

The Impact of Assumed Uncertainty on Long-Term Decisions in Forest Spatial Harvest Scheduling as a Part of Sustainable Development

“…Unfortunately, this characteristic could not be incorporated into the harvest scheduling process by the presented approach because of the nature of RO. A potential approach is called multi-band robust programming, as proposed by D'Andreagiovanni and Raymond [33].…”