Forest fibre network design with multiple assortments: a case study in Newfoundland
Bibliographic record
Abstract
The Canadian forest industry is facing several challenges including high fibre cost, decline in commodity profitability, and low investment levels at relatively old manufacturing plants. To enable transformation of the industry, innovations are needed to develop value-added products and to shift to an efficient integrated value chain. In this regard, improved logistics for better handling of raw material, forest biomass utilization, and use of new technologies are some promising avenues. In this paper, we propose a generic value chain model that includes locating new sorting yards and biorefineries maximizing the overall profit of the value chain. This integrated planning problem deals with strategic decisions including investments in new facilities and technologies and tactical decisions comprising backhaul transportation and fibre flows across the value chain. To solve such a problem, we developed a mixed integer programming model to design the forest value chain network. This model is used in an industrial case study in the province of Newfoundland, Canada. We have generated and analyzed 32 scenarios evaluated on 12 predefined key performance indicators. The results show that collaboration through backhauling, common terminals, and new assortments are important opportunities to improve the profitability and efficiency of the value chain. The potential improvement over the current situation is as high as 23% considering the aforementioned actions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".