Routing Courier Delivery Services with Urgent Demand
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
Courier delivery services in many cases are faced with sporadic, tightly constrained, urgent requests in addition to regular demand. An example of such an application is the transportation of medical specimens, where an efficient delivery is crucial in providing high quality and affordable service. However, the presence of random urgent requests, due to medical emergencies, can create substantial additional costs if not taken into account.We model this problem as a multi-trip vehicle routing problem with time windows using stochastic programming with recourse to represent the random urgent requests. We develop an insertion based heuristic with a tabu-search improvement phase to solve this problem. Our computational results show that this approach obtains significant improvement in travel costs as well as in route similarity over alternative methods, both on randomly generated data as well as on a real data set provided by a leading healthcare provider in Southern California.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| 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