Skip to Main Content
A chance constraint estimation approach to optimizing resource management under uncertaintyAuthor(s): Michael Bevers
Source: Canadian Journal of Forest Research. 37: 2270-2280.
Publication Series: Miscellaneous Publication
PDF: View PDF (155 B)
DescriptionChance-constrained optimization is an important method for managing risk arising from random variations in natural resource systems, but the probabilistic formulations often pose mathematical programming problems that cannot be solved with exact methods. A heuristic estimation method for these problems is presented that combines a formulation for order statistic observations with the sample average approximation method as a substitute for chance constraints. The estimation method was tested on two problems, a small fire organization budgeting problem for which exact solutions are known and a much larger and more difficult habitat restoration problem for which exact solutions are unknown. The method performed well on both problems, quickly finding the correct solutions to the fire budgeting problem and repeatedly finding identical solutions to the habitat restoration problem.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationBevers, Michael. 2007. A chance constraint estimation approach to optimizing resource management under uncertainty. Canadian Journal of Forest Research. 37: 2270-2280.
Keywordsresource management, chance-constrained optimization, natural resource systems, heuristic estimation method
- Reserve design to maximize species persistence
- The Steiner Multigraph Problem: Wildlife corridor design for multiple species
- Deploying wildland fire suppression resources with a scenario-based standard response model.
XML: View XML