Strategic planning in a forest supply chain: a multigoal and multiproduct approach
Bibliographic record
Abstract
Supply chain management problems are widespread across all economic activities. We analyze here how to address these in the case of the forest industry, which in emerging economies such as Argentina is subject to high logistic costs and faces problems of biological and economic sustainability. In this work, we analyze a management model covering from the schedule of harvesting activities and the transportation of raw materials to the final transformation at several industrial plants. Since this involves more than one objective, single-criterion mathematical programming methods are not appropriate. Here, instead, we introduce an extended goal programming formulation of the problem, able to yield good solutions in a computationally efficient way. We consider four goals: the maximization of the net present value of the production, the minimization of interannual variations in harvests, the maximization of carbon capture in the form of forest biomass, and the minimization of variations in the mean annual distance covered in transportation to the industrial plants. We apply this theoretical model to derive solutions for an actual Argentinean company. We show that the model reaches the target levels of the different goals, except for carbon balance, which is negative in all of the scenarios under evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".