Article ID: | iaor19912136 |
Country: | Switzerland |
Volume: | 31 |
Start Page Number: | 511 |
End Page Number: | 526 |
Publication Date: | Mar 1991 |
Journal: | Annals of Operations Research |
Authors: | Pickens James B., Hof John G., Kent Brian M. |
Keywords: | forestry |
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Because of the nature of forestry, this application has several unique characteristics. For example, the models consider many different management actions that take place over many years, thus resulting in very large LP formulations with diverse data. In addition, almost none of the data are known with certainty. The most pervasive occurrence of stochastic information is in the production coefficients, which indicate the uncertain response of the managed forest ecosystem to various management options. A ‘chance-constrained’ approach to handling this uncertainty would often be appropriate in forestry applications-managers and decision makers would like to specify a probability with which uncertain constraints are met. Unfortunately, chance-constrained procedures for