Assessing the on‐road route efficiency for an air‐express courier
Bibliographic record
Abstract
Abstract This paper proposes an EXO‐CAT data envelopment analysis (DEA) model to evaluate the efficiency of on‐road activities (pickup and delivery) for an air‐express courier on a route‐by‐route basis. The proposed model combines the constraints of exogenously fixed inputs DEA and categorical DEA to account for the continuous and discrete external environmental factors affecting the courier route efficiency. We select labor, route length (a proxy of fuel consumption), and vehicle capacity as the inputs; number of documents delivered, number of boxes delivered, number of documents picked‐up, and number of boxes picked‐up as the outputs. A case study with 248 on‐road routes currently operated by an air‐express courier in Taiwan is undertaken. It is found that stop density, travel speed, and service area type have significant influences on the couriers' route efficiency. Based on the detailed DEA results, the managers do not need to perform check‐rides for all routes; instead, they need only to focus on the most inefficient ones. Such DEA results can also be applied to develop new projects or make judgments on investing any new on‐road routes. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".