Developing planning and collaboration models for the efficient integration of truck platoons in forestry transportation
Bibliographic record
Abstract
Truck platooning consists in forming a convoy of two or more trucks traveling close to one another, synchronized in their movements using automated driving technology and vehicle-to-vehicle (V2V) communication. The following trucks can automatically adjust steering, speed, and braking based on the actions of the lead truck. This leads to reduced fuel consumption and depending on the automation level to less truck drivers. This doctoral thesis investigates the potential of truck platooning technology in improving the efficiency and sustainability of product transportation in particular in forestry. It is divided into three phases. The first phase presents a systematic literature review on truck platooning transportation planning. This review explores the models used in the literature for different planning levels (notably in collaboration contexts), the benefits of truck platooning transportation planning alongside the challenges it faces. According to the review, more than 80% of the papers were published between 2019 and 2023. The study highlights the lack of strategic-tactical planning and collaboration models focused on the forestry industry, as well as models that address the integration of truck platoons at long-term and mid-term planning levels. The second phase focuses on developing a Mixed-Integer Linear Programming (MILP) model for evaluating the efficiency of truck platooning in upstream forest supply chains. It explores the gradual integration of truck platooning into the transportation network. Moreover, this phase analyzes truck platooning benefits (cost savings, fuel consumption, and labor), as well as the factors that influence its efficiency. This phase demonstrates potential cost savings between 3% to more than 20% and fuel consumption reductions of 1-16%, and reductions in the number of drivers between 3% to more than 50% depending on the scenario notably level of platooning integration to the transportation network. The key factors influencing platooning efficiency are average transportation distances, backhauling opportunities, and access levels of truck platoons to forest areas. The last phase focuses on collaboration between carrier companies using truck platooning. The results indicate that collaboration using truck platooning can yield additional cost savings of 1-19%. This phase analyzes different collaboration scenarios. Moreover, it investigates cost-sharing between the companies in the collaboration. It provides an MILP transportation planning model and uses Game Theory based models for cost sharing. Phases 2 and 3 present applications to case studies inspired from upstream forest transportation networks in the province of Quebec, Canada. This doctoral project presents decision-making models that consider the characteristics (benefits and constraints) of truck platooning that can be implemented in the real-world to reduce transportation costs, fuel consumption, and the number of drivers required (in a labour shortage context). This contributes to more sustainable forest transportation.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".