Stochastic Optimization Model for Smart Freight Matching and perspective of the application in Montreal
Bibliographic record
Abstract
Smart freight platforms are emerging as a key component of the new sustainable and smart mobility paradigm. These platforms enable efficient and flexible matching of freight demand and supply, reducing costs and environmental impacts. However, the matching process is subject to various uncertainties, such as weather, traffic, and truck failures, which can affect the performance and reliability of the platform and provided services. In this study, we develop a two-stage stochastic optimization model for matching smart freight platform that considers these uncertainties. The model aims to maximize the matching rate of the platform while satisfying the service level requirements of the customers and minimizing the environmental impacts considering the consolidation. We apply the model to the case of Montreal, Canada, using a simulated data set that reflects the characteristics of the city’s freight market. The results show that the model can improve the matching quality and robustness of the platform under different scenarios of uncertainty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".