Developing planning and collaboration models for the efficient integration of truck platoons in forestry transportation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».