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Knowledge-based approaches to forest operations scheduling problems

University of British Columbia

Operation scheduling in fast growing, intensively managed forest plantations is characterized by diverse qualitative and quantitative goals and constraints. These goals and constraints may be temporal or spatial, and may interact in complex ways. Traditional linear programming approaches to forest operations scheduling generally require significant simplifications to the problem statement before they can be solved and do not provide managers with easily understandable solutions except in simple cases. A series of knowledge-based models was developed to assist forest managers in operations scheduling problems. These knowledge-based models included random and heuristic search models using different knowledge amounts, and expert systems. The models used silvicultural knowledge derived from plantation management plans of the Forestry Commission of New South Wales to construct practical and feasible thinning and harvesting regimes for each stand in a plantation. Other knowledge was derived from game and puzzle solving domains and human experts in operations scheduling to help construct forest operations schedules that simultaneously considered some stand and forest management and environmental considerations. Operations schedules produced for two intensively managed and rapidly growing plantation forests by the knowledge-based models were evaluated for timber flow, stand health, scenic beauty, and water quality. Schedules were found that were within 5% of the optimal timber flow found by (integer) linear programming approaches. The knowledge-based model solutions were superior to the linear programming solutions for at least one of the health, beauty or water quality considerations, and were at least as good as solutions produced by human operations scheduling experts. The knowledge-based models were used to explore the relationships between the various goals and objectives. The knowledge-based approach was also used to develop robust strategies in the presence of uncertainty in the growth models. Important stand I regime combinations were isolated to allow management to reduce the impact of uncertainty. The quality of the knowledge-based model solutions depended upon the specific knowledge included, and the forest structure. However, more knowledge did not necessarily lead to a better solution. In the larger plantation forest examined, additional knowledge did not lead to a better solution relative to the solution generated by a model using little knowledge. This was because the additional knowledge lacked key information about the forest age class distribution and problem size. Without this information, the additional knowledge was incomplete and directed the model- solution inappropriately. The knowledge-based models developed are simple and easy to understand. They can be used to integrate silvicultural knowledge with other forestry domain knowledge to produce plans that can be understood and defended. They can also serve to integrate future knowledge development and show the potential advantages of research.

Thesis/Dissertation

application/pdf

2008-12-20

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http://hdl.handle.net/2429/3265

Forestry

University of British Columbia

UBCV

DSpace