Show simple item record

dc.contributor.author Sianturi, Maikel en_US
dc.date.accessioned 2007-05-18T20:01:11Z
dc.date.available 2007-05-18T20:01:11Z
dc.date.issued 2000-09-01T00:00:00Z en_US
dc.identifier.uri http://hdl.handle.net/1993/1859
dc.description.abstract People want to use forests for their benefits as much as possible but environmental impacts of their actions should be minimized. This leads to difficult land management problems with multiple, conflicting objectives. Forest land management analysts have developed and utilized sophisticated planning methods to address complex issues involving multiple objectives. An intensive literature review of these techniques is presented. The most popular multiobjective technique among forester is Goal Programming. Multiobjective Genetic Algorithms are relatively new optimization techniques which have not yet been used in forestry. Two multiobjective forestry problems are solved using a Multiobjective Genetic Algorithm and the results are compared to Goal Programming solutions. It is shown that the Multiobjective Genetic Algorithm can find solutions with better tradeoffs between conflicting objectives. en_US
dc.format.extent 7759150 bytes
dc.format.extent 184 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language en en_US
dc.language.iso en_US
dc.title Operations research applied to forestry management en_US
dc.degree.discipline Mathematics, Computational & Statistical Sciences en_US
dc.degree.level Master of Science (M.Sc.) en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

View Statistics