Approximate Dynamic Programming Captures Fleet Operations for Schneider National
Why this work is in the frame
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Bibliographic record
Abstract
Schneider National needed a simulation model that would capture the dynamics of its fleet of over 6,000 long-haul drivers to determine where the company should hire new drivers, estimate the impact of changes in work rules, find the best way to manage Canadian drivers, and experiment with new ways to get drivers home. It needed a model that could perform as well as its experienced team of dispatchers and fleet managers. In developing our model, we had to simulate drivers and loads at a high level of detail, capturing both complex dynamics and multiple forms of uncertainty. We used approximate dynamic programming to produce realistic, high-quality decisions that capture the ability of dispatchers to anticipate the future impact of decisions. The resulting model closely calibrated against Schneider's historical performance, giving the company the confidence to base major policy decisions on studies performed using the model. These policy decisions helped Schneider to avoid costs of $30 million by identifying problems with a new driver-management policy, achieve annual savings of $5 million by identifying the best driver domiciles, reduce the number of late deliveries by more than 50 percent by analyzing service commitment policies, and save $3.8 million annually by reducing training expenses for new border-crossing regulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it