Operations research applied to forestry management
| 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.degree.discipline | Mathematics, Computational and Statistical Sciences | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| 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.identifier.uri | http://hdl.handle.net/1993/1859 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.title | Operations research applied to forestry management | en_US |
| dc.type | master thesis | en_US |