AN ANALYSIS OF THE ASSIGNMENT OF DELIVERY ROUTES TO VEHICLE DRIVERS IN STOCHASTIC VEHICLE ROUTING OPERATIONS
Bibliographic record
Abstract
Random day-to-day fluctuations in customer demands extend the range of decisions to be made by managers of vehicle routing/dispatch operations. For one, dispatch/routing managers must decide how responsive the delivery routes should be to the stochastic demands. But even with that decision settled –often by using daily route reoptimization to maximize responsiveness– the assignment of drivers to the reoptimized delivery routes must also be determined. In the interest of customer service, managers may use driverto-route assignment rules that ensure that the driver who is historically most familiar with a given customer will most likely be chosen to continue serving the route that that customer is on. Using data from several vehicle routing scenarios, this paper presents a statistical analysis of one such decision rule, and uses the analysis to derive managerial implications of rules that seek to maximize customer-driver familiarity. The paper also provides some preliminary insights on the potential for Markov Chains in modeling driver-to-route assignment decisions.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".