Collaboration between carrier companies using truck platooning: an application in the forestry industry
Bibliographic record
Abstract
This study explores collaboration between different carrier companies where at least one is equipped with hybrid truck platooning technology (one driver operates a platoon of trucks). Collaboration could help carrier companies share resources and reduce costs. First, a tactical transportation planning problem is formulated as a Mixed-Integer Linear Programming (MILP) model. Using a mix of ordinary and platoon trucks for collaboration, this model aims to minimize transportation costs. The decisions to be made include choosing direct and backhaul routes for both types of trucks in the transportation network and potential terminal nodes to activate. The results show that using truck platooning in collaboration could lead to cost savings ranging between 0.5% and 19% (compared to only using ordinary trucks) depending on the level of collaboration and coverage between the transportation networks of the companies involved. Second, we study the cost-sharing problem to ensure fair cost-saving allocation between the companies. We compare the results of four cost-sharing methods used in a two-step cost allocation process. The first step allocates the cost savings obtained from collaboration using only ordinary trucks. The second step allocates additional cost savings due to platoon trucks. The results show the Shapley value method produces the best allocations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".