A discrete-event simulation model to test multimodal strategies for a greener and more resilient wood supply
Bibliographic record
Abstract
Increasing occurrences of natural disturbances, including windstorms and high snow cover, and supply chain risks lead to severe irregularities in wood harvest and transport. To overcome resulting supply difficulties, innovative multimodal systems via rail terminals are promising options offering the potential to increase buffer capacity, improve supply chain resilience, and reduce greenhouse gas emissions. Therefore, a train terminal is included in a virtual simulation environment spanning the entire wood supply chain from forest to industry to test, analyze, and evaluate a complex multimodal system in different scenario settings. Furthermore, the simulation model provides intuitive decision support through animation and a cockpit of key performance indicators, facilitating hands-on workshops with supply chain managers. Results show the advantage of a combination of unimodal and multimodal transport in the wood supply chain of the observed case-study region. This combination proves to be resilient and outperforms other tested supply chain strategies by avoiding both bottlenecks and ill-timed plans and reducing carbon dioxide (CO2) emissions. Furthermore, workshops conducted with industry experts indicate that adapting collaborative supply chain control strategies by means of a participatory simulation environment enhances the development of advanced risk management and therefore improves supply chain resilience, efficiency, and sustainability.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".