Emergency Routing and Structural Optimization of E-commerce Logistics Network for Parcel Transportation Based on Multiple Models
Bibliographic record
Abstract
The adjustment measures include closing or opening new routes, but not adding new logistics sites. To achieve dynamic adjustment of the logistics network's route structure, including the closure or development of new routes, the aim is to minimize the number of routes affected by changes in cargo volume before and after the closure of DC9, while maintaining a balanced workload among the routes. Therefore, an Ant Colony Optimization (ACO) algorithm model is established, and MATLAB and SPSSPRO are utilized to solve the prepared table based on the ACO model. The obtained routes DC69→DC5, DC69→DC8, DC69→DC14, and DC69→DC62 have a cargo conformity rate of 97%, with an average route workload of around 7%. The remaining cargo across all routes is 11,280.7. This indicates that the overall results remain unaffected after deleting DC9 and adding the new route DC3→DC1, with no routes exceeding the required conformity, satisfying the practical requirements. Next, an evaluation is conducted to assess the importance of different logistics sites and routes within the network. Taking into account basic conditions, such as parcel quantities, transport frequencies, maximum transport capacities, transfer capacities, and other influencing factors, a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis model is constructed. The processed table is used to analyze the network's robustness, determining appropriate settings for processing and transport capacities. The objective is to reduce the overall operating costs of the network while ensuring a more balanced distribution of network workload.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".