Optimization of harvest planning of forest stands infested by Spruce Budworm using stochastic programming approach
Bibliographic record
Abstract
In the forest industry, harvesting process is one of the key critical processes as it supplies the primary raw material for different mills. However, due to several natural disturbances such as insect outbreaks, the impact and the effects on the tactical planning of forest supply chain can be irreversible. We consider the susceptibility, vulnerability, and increasing mortality by defoliation in trees over time caused by Spruce Budworm (SBW) infestation. The aim of this project is to use advanced optimization methods, in our case Stochastic Programming (SP), to maximize the market value of the harvested logs considering the occurrence of infestation over all the possible infestation scenarios. In our research method, we formulate a deterministic Mixed Integer Linear Programming (MIP) model which has then been extended into a Two-Stage SP model to deal with uncertainty related to the severity and propagation of the infestation; we also, as well, track the levels of infested volume inventory of the forest stands under the phases of SBW infestation according to their life cycle. The models are implemented in the modelling language of AMPL and solved using the commercial CPLEX solver. We tested the model for analyzing preliminary results to show the value of using SP in planning under uncertainty and the cost of the information. Then, we applied the model to a real case study in the North Shore region of the province of Québec (Côte-Nord) and compared deterministic and Stochastic Optimization (SO) methods with standard metrics for their evaluation. More precisely, we compute the Expected Value with Perfect Information (EVPI) and Value of Stochastic Solution (VSS) parameters, to analyze whether the method of Stochastic Programming is adequate for the project and the cost of the quality of the information and when we do not consider uncertainty. The optimization models offer better decision-making in forest management, reduce costs, increase the value in the entire chain and loss of trees as Spruce Budworm can lead to future outbreaks. Finally, we suggest some insights of the uncertain parameter that can affect the results of the optimization models and explain some suggestions that could improve the model if other attributes are included in the harvesting planning and the relevance of including other uncertainty parameters in forest planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".