Optimization of Drop-and-Pull Transport Network Based on Shared Freight Station and Hub-and-Spoke Network
Bibliographic record
Abstract
The current drop-and-pull (D-P) transport process has many defects, including but not limited to the insufficient information sharing, the private ownership of vehicles and infrastructure, and the mismatch between vehicles and goods.Moreover, the hardware and software of existing freight stations fall short of the demand for D-P transport.To solve these problems, this paper optimizes the design of the D-P transport network based on shared freight station and the hub-and-spoke (H-S) network.The freight stations were taken as the hubs, and the routes between supply/demand point and freight station are treated as spokes.On this basis, an optimization model was established to minimize the total cost of freight stations and maximize the force from freight stations on supply/demand points in the H-S D-P network.In addition, all the supply/demand points in the region are covered by the selected freight stations.The LINGO software was introduced to solve the established model.Taking a region in southern China for example, the proposed shared freight station design was compared with the traditional freight station design.The results show that the single-hub H-S D-P network obtained by the traditional design could meet the demand when the D-P demand was relatively small; however, only the multi-hub H-S D-P network obtained by the shared freight station design could fulfil a large D-P demand in an efficient manner.The research findings show that the shared freight station is the future of D-P transport.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".