Optimizing truck platooning transportation planning: an application to forestry products supply chains
Bibliographic record
Abstract
The Fourth Industrial Revolution offers new opportunities for improving the efficiency and agility of supply chain operations such as transportation. This study explores the impact of integrating truck platooning technology in forestry products supply networks. Companies need to know how and where to use truck platooning in transportation networks to get optimum benefits from truck platooning in supply chains. To this end, a Mixed Integer Linear Programming model was developed. Decisions to be made include the selection of the potential terminal locations, the number of ordinary and platooning trucks needed in the transportation network, the origin and destination of products, and their flow in direct and backhaul routes. The objective is to minimize the overall transportation cost, including terminal location costs, fixed costs for ordinary and platoon trucks, fuel, and driver costs. A total of 27 randomly generated instances are used to test the optimization model. We considered several scenarios to analyze different combinations of using or not backhauling, having or not truck platoons in the network, and allowing or not the truck platoons to visit the forest areas. The results show that the potential savings of combining the two types of trucks are in the range of 1%–12% in the scenarios in which truck platooning transportation is allowed only between terminal and mill nodes. This savings could reach more than 20% when the truck platoons are allowed to visit forest areas, depending on how many forest areas are visited. The number of drivers can be reduced by 3% to more than 30%. In addition, using truck platooning and backhauling together could reduce fuel consumption by 15.6% on average.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".