Learning-Based Matching Algorithm for Smart Freight Platform and Sustainability Assessment in Montreal
Bibliographic record
Abstract
Road freight transportation connects producers and consumers, and delivers goods in a timely manner, which is essential for the economy and society.However, traditional freight forwarding faces challenges such as long delays, high labor costs, and empty mileage, which increase the logistics and environmental costs and affect the carriers and shippers negatively.To address these issues, we develop a sustainable smart freight-matching model using Reinforcement Learning (RL).The main components of matching platform include: (i) optimizing real-time carriershipper matching using RL, aiming to maximize matches while considering time, location, and capacity (ii) emphasizing designing a dynamic dispatching system to ensure on-time availability, optimize resource allocation, enhance customer satisfaction, and improve operational efficiency in freight fleet management, (iii) stablishing dynamic cargo consolidation to reduce shipping costs, lower CO2 emissions, and enhance vehicle and logistics resource efficiency.To achieve sustainability, this platform maximizes platform profit as economic criterion, minimizing vehicle emissions, as environmental criterion and maximizing service level, as a social criterion.We use the Actor-Critic framework to model the state, action, and reward of the smart freight platform.We use Montreal region as a case study to test our model.We create synthetic data that simulates the real-world characteristics of freight logistics, such as timestamps, player roles, and location coordinates.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".