What is the Right Size for Truckload Carrier Alliances?
Bibliographic record
Abstract
In spot markets for truckload transportation services, centralized collaboration among carriers is often regarded as an ideal way to determine which load (a.k.a. shipment) is delivered by each carrier. The challenge of getting all competing carriers to collaborate under a centralized system (instead of being rivals for loads) has prompted interest in collaboration modes that are on a smaller scale than complete centralization. In this research vein, this paper answers the following question: for the small-scale collaboration of decentralized load exchange among small alliances of willing carriers, how close do the performance results (profits, etc.) come to the purported ideal results under centralization ? Our core finding from extensive computational experiments is that, by collaborating with the right load exchange partners, a carrier in a small and easier-to-manage alliance can achieve financial savings that closely match and sometimes even surpass the per carrier savings from a fully centralized system. This, and some closely related insights, comprise this paper’s main contributions to the literature. The practical relevance of the contributions is in facilitating decisions about how much effort is worth expending on having every carrier within geographic network participate in a centralized system.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".