A robust optimization approach protected harvest scheduling decisions against uncertainty
Bibliographic record
Abstract
Harvest scheduling decisions are made in an uncertain environment, and current modeling techniques that consider uncertainty impose severe difficulties when solving real problems. In this paper we describe a robust optimization methodology that explicitly considers randomness in most of the model coefficients while keeping the model computationally tractable. We apply the method to schedule harvest decisions when both timber yield and demand of two products are uncertain. Since uncertain coefficients must be independent, uniform, and symmetrically distributed, we only address uncertainty attributable to estimate errors of forecast models. The methodology was applied to a 245 090 ha forest in British Columbia, Canada. We compared the change in harvest decisions and objective function when robust solutions are implemented relative to deterministic solutions. Although probability bounds can be used to a priori define the probability of constraint violations, they produce conservative solutions. We therefore tested the rates of constraint violations by simulation. While traditional deterministic decisions were always infeasible when uncertain data were simulated, robust decisions were much less sensitive to uncertainty and were, to a large extent, protected against the occurrence of infeasibilities. In exchange, reasonable reductions in the objective function were observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".