Planification tactique du cargo aérien : comparaison entre deux formulations en programmation linéaire
Bibliographic record
Abstract
Cargo companies, just like any other company, are striving to do better and be more profitable.In order to do so, this continuous improvement has to include strategic decisions about evaluating and adapting the network.The company has to study the optimal shipment of the forecasted demand in the network for the following season.Given a set of demands to be transported from origins to destinations and a set of flights, the objective is to deliver the freight to destination through the network as efficiently as possible.However, operations are constrained by the physical characteristics of the infrastructure and the operational policies of the airports.To this end, we describe two different formulations of the problem and their implementations.At this level of planning, the aim is to determine an efficient allocation of physical and human resources to improve the transportation system.As we are not concerned with day-to-day operations, data is aggregated to decrease the size of the problem and also to improve the demand forecasting.Once the demand is aggregated, it needs to be transported through the existing service network.To do so, we created two linear programming mathematical models.The main goal is to compare their efficiency.The first formulation, flow variables are defined on arcs, whereas in the second one, flow variables are defined on paths.This study showed that path-based models are more efficient when it comes to solving large-scale problems such as this industrial case.Also, an a priori generation of paths is more convenient to formulate complex non-linear constraints.In addition to that, Air Canada Cargo has an entirely functional router that takes into consideration all the problem constraints.v
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".