Development of a Genetic Algorithm Model for a Multiple-Objective Forest Harvesting and Zonation Problem

• Author / Creator

  Kuloglu, Tevfik Z

• Most of the forested lands in North America are managed for multiple
  objectives and forest management plans are designed in a manner to obtain or
  maintain sustainable forest management certification. One strategy for achieving
  some of these goals is forest zonation, which allows for different intensities of forest
  management (including reserve areas) in different zones of the forest. The focus
  of this thesis is the development of a spatially explicit forest estate model which
  simultaneously allocates forest land to different management intensity zones and
  harvest periods with the goal of satisfying multiple management objectives. The
  model was initially developed using mixed integer goal programming. This helped to
  identify a basic model structure which was used to guide the development of a genetic
  algorithm-based implementation. The development of the model, particularly the
  setting of goal weights to describe the decision maker's preferences is described in
  detail.

• Subjects / Keywords

  • Genetic algorithms
  • Mixed integer programming model
  • Forest Zonation
  • Multiple-Objective

• Graduation date

  Fall 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3239X

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.