A Bilevel Programming Model for Designing a Collaborative Network for Regional Railway Transportation and Logistics: The Case of the Beijing‐Tianjin‐Hebei Region in China
Bibliographic record
Abstract
For the coordinated development of regional logistics, there is a need to break down the administrative barriers between cities and build an efficient and smooth regional logistics network. Integrating regional logistics resources to achieve cross‐regional optimal allocation is essential for efficient logistics networks. The accessibility and high capacity of the railway transportation network make it possible and necessary for the coordinated construction of railway transportation and logistics networks. This paper studies a bilevel programming model to analyse the collaborative network planning process of railway transportation and logistics. The objective is to take into account the existing layout of regional railway traffic network and the future needs of regional logistics. For the upper level of the bilevel programming model, the social, economic, and logistics service index of regional railway traffic nodes is considered to determine the numbers and locations of radiation of the cities. Meanwhile, the lower programming model uses a logistics gravity model, 0‐1 integer programming model, and logistics membership degree to calculate the logistics correlation between node cities and the radiation range of the hub cities. An empirical study of the Beijing‐Tianjin‐Hebei region in China shows the process and effectiveness of the bilevel programming model. Four distinctive logistics radiation circles, five trunk lines, nine hub‐spoke branch lines, and six branch lines are suggested. A railway traffic‐oriented regional logistics network provides a reference for regional railway traffic planning, thereby optimizing the allocation of regional logistics resources, improving the level of regional logistics services, and contributing to the development of the regional logistics industry.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".