New large-scale data instances for CARP and new variations of CARP
Bibliographic record
Abstract
The capacitated arc routing problem (CARP) captures important aspects of real-life problems and has been studied extensively over the past two decades. Based on a waste collection project, we introduce a number of new CARP variations. We first present three multi-compartment CARP variations of different levels of complexity regarding compartments and where one incorporates a time horizon. We then present a variation that seeks to coordinate vehicles over a planning horizon such that the vehicles that collect different waste fractions from the same households do so on the same day of the week. Finally, the semi-periodic CARP takes into account that the households on a street, providing the demand of the edge, may not request waste collection at the same interval. We present large-scale instances both for the classical CARP and for the five new problems. The instances are based on real-life networks and waste data from five areas in Denmark and cover rural as well as urban areas. The largest instances contain more than 10,000 nodes. We give detailed information about the construction of the instances from the real-life data, and explain how they can be used to perform scenario analyses.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".