The two-echelon vehicle routing problem with backhauls and capacitated satellites
Bibliographic record
Abstract
The present work aims to solve a 2-Echelon Multiple-Trip Vehicle Routing Problem jointly considering Capacitated Satellites and Reverse Flows (2E-MTVRP-CSRF). It fills two gaps in the two-echelon VRP literature: First, to the best of our knowledge, only inbound distributions activities were addressed in 2E-VRP. Second, the capacity at intermediate facilities (also called satellites) is generally considered either unbounded or null.We propose a decomposition-based matheuristic. It iteratively optimizes the first and the second echelon, by fixing the other echelon, and creates new complete solutions by solving a column-based MILP formulation with the set of generated trips. The optimization of each echelon is performed with tailored Small and Large Neighborhood Search heuristics.Our numerical experiments highlight that using medium-size satellites achieves a good trade-off between the routing cost and the cost of storage spaces in city-cores. In addition, we found that the integration of forward and backward flows in this type of setting reduces the routing costs by 34%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".