Hierarchical forest management with anticipation: an application to tactical–operational planning integration
Bibliographic record
Abstract
This paper examines the problem of harvest capacity planning at a tactical level. Annual capacity planning allows planners to determine the number of contractors to hire per period throughout the year and to define the duration of their contracts. In practice, this process usually involves the analysis of historical data regarding the operational use of capacity and aggregated demand forecast, the output of which then serves to plan harvest operations. Although this form of hierarchical planning reduces the complexity of the task, the decomposition into subproblems that must be successively resolved can lead to infeasibility or poor use of harvesting capacity. The specific problem addressed here resides in how one can consider the operational impact of harvesting decisions taken at the tactical level to ensure a plan’s feasibility at the operational level. We present a tactical planning process based on Schneeweiss’ generic hierarchical modeling approach. A computational experiment demonstrates how a tactical planning process is influenced by the input of the operational level anticipation model. The anticipation approach we propose appears to be a valid method to better integrate key operational-level decisions into tactical plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".