Resumen
The paper shows how the aspects of uncertainty in spatial harvest scheduling can be embedded into a harvest optimization model. We introduce an approach based on robust optimization that secures better scheduling schematics of the decision maker while eliminating a significant portion of uncertainty in the decisions. The robust programming approach presented in this paper was applied in a real management area of Central Europe. The basic harvest scheduling model with harvest-flow constraints was created. The uncertainty that is assessed here is due to forest inventory errors and growth prediction errors of stand volume. The modelled results were compared with randomly simulated errors of stand volume. The effects of different levels of robustness and uncertainty on harvest-flow were analyzed. The analysis confirmed that using the robust approach for harvest decisions always ensures significantly better solutions in terms of the harvested volume than the worst-case scenarios created under the same constraints. The construction of a mathematical model as well as the methodology of simulations are described in detail. The observed results confirmed obvious advantages of robust optimization. However, many problems with its application in forest management must still be solved. This study helps to address the need to develop and explore methods for decision-making under different kinds of uncertainty in forest management.