Examining the impact of transfers in pickup and delivery systems
Bibliographic record
Abstract
As an attractive feature for modern transportation systems, the potential of the transfers capability (the load/passenger transfer between the two vehicles in its route) in reducing costs, increasing customer satisfaction and increasing the flexibility of the system, has been approved.But how profitable it could be under different circumstances?In other words, to which factors its influence depends on?what are its benefits versus its costs?The present research aimed to give a relatively comprehensive answer to these questions using a mathematical model of the pickup and delivery system with transfers.According to the model results under different situations, many factors such as modeling assumptions, system goals, transportation network scheme, vehicle fleet in terms of capacity, cost rate, and time window of activity and requests in terms of the length (direct distance between the pickup and delivery points), time windows and the volume to vehicle capacity ratio, affect the transfers benefits.As the small-scale numerical results indicate, we have an average of 5.7% reduction in the trip cost under normal conditions, which increases with the heterogeneity of vehicles, shorter time windows, and an increase in the length of the request.On the other hand, it is expected that profitability increases by problem size. .,
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".