Combining optimization and simulation tools for short-term planning of forest operations
Bibliographic record
Abstract
The use of optimization techniques is well established in forest short-term planning and decision-making. Yet, existing techniques may pose some limitations for tackling with stochastic factors impacting in the execution of forest operations, such as delays, equipment breakdowns and other unexpected events. This paper explores the potential of using optimization techniques in combination with discrete-event simulation (DES) models for planning harvesting and logistics operations acknowledging uncertainty. DES models may be useful for assessing the performance and identifying bottlenecks associated with the execution of the deterministic plans retrieved with optimization techniques, when such stochastic events occur. This paper further presents an approach for the combination of a heuristic and a DES model developed in SIMIO. This approach was used to solve the raw material reception problem (RMRP) at a Portuguese pulp mill. This paper concludes with the analysis of the performance of deterministic schedules for the wood trucks considering uncertainty in their arrival at the mill.
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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 | 0.002 | 0.003 |
| 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".