MEASURING AND MANAGING THE LEARNING REQUIREMENTS OF ROUTE REOPTIMIZATION ON DELIVERY VEHICLE DRIVERS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Outbound logistical systems that are designed with the flexibility to perform daily reoptimization of delivery routes are often touted as the systems of choice in dealing with randomly fluctuating (stochastic) customer demands. However, a potential drawback with such systems is that the day‐to‐day changes in the delivery routes force each driver to traverse routes that extend beyond the region required if customer demands remained stable. That is, the efficient completion of deliveries under route reoptimization imposes an additional requirement on drivers to learn these routes. Quantification and analysis of this additional learning requirement, along with some of the associated human resource management implications, comprise the paper's primary focus. A key contribution of the research is that the analysis accounts for the cost‐effectiveness of a vehicle routing tactic that might be used to reduce the learning burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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