A Simulated Annealing Approach with Spatial Operator for Spatial Forest Thinning Planning

• Main Authors: Su, Ke-Hong, 蘇科閎
• Other Authors: Lin, Chun-Cheng
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/h7e694

碩士 === 國立交通大學 === 工業工程與管理系所 === 104 === Aside from economic aspect, spatial forest planning is also concerned with being in harmony with ecological and environmental protection with spatial concerns. At-mospheric CO2 level directly affects climate change, and further, indirectly affects the whole ecosystem and human. Forests are capable of sequestrating carbon via photo-synthesis, and hence, increasing forests can decrease the CO2 level in the atmosphere. To lessen effects of global warming, this work investigates the spatial forest thinning planning problem that takes into account carbon sequestration and emission simulta-neously, which were never considered in previous studies. The problem is to decide different forest thinning schedules at different planning periods. Forest thinning is the selective removal of forests. Appropriate forest thinning is beneficial for continuing forest growing, increasing forest timber harvested, and protecting eco-logical envi-ronments, to achieve cycling and sustainability of forest resources. This work first es-tablishes a mathematical programming model for solving small-scale problems, in which the model with carbon sequestration, carbon emission, and forest thinning is increasingly complex. Furthermore, to solve large-scale problems, this work proposes an improved simulated annealing algorithm. The first improvement in the proposed algorithm is to additionally consider a spatial-operator-based local search scheme, which searches solutions based on the spatially neighboring idea that a certain forest block may adopt the same forest thinning schedule with its neighboring forest blocks. The second improvement in the proposed algorithm is to filter the solution that is the same as the current solution to avoid the time wasted for searching repetitive solutions, when searching a neighboring solution during algorithm process. Simulation results for 300 forest instances show that the proposed algorithm has a statistically remarkably better performance than conventional algorithms. Additionally, the decision with carbon considerations is also verified to be obviously beneficial to the environment.