Continent-scale inventory routing solutions
Bibliographic record
Abstract
This dataset of inventory routing solutions is a fruit of our partnership between Renault Group and the CERMICS laboratory at Ecole des Ponts. The related instances (also publicly available) are continent-scale with hundreds of customers, 21 days horizon, and 15 depots on average. Routes can last several days (continuous-time), and 30 types of commodities are involved, leading to bin packing problems when filling trucks. In our paper "Solving a Continent-Scale Inventory Routing Problem at Renault" we introduce a new large neighborhood search to solve those instances. This dataset contains the solutions provided both by our algorithm and by a benchmark we implement, as shown in the computational experiments section of our paper. We hope that sharing them publicly will motivate research on real-world and large-scale inventory routing. Environmental and economical impacts at stake are substantial.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.024 |
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".