Solution of Two-Echelon Facility Location Problems by Approximation Methods
Bibliographic record
Abstract
The two-echelon delivery structure is a strategy that can be implemented in urban areas to lower delivery costs by reducing the movement of heavy goods vehicles. In a two-echelon delivery structure, large trucks deliver shipments from a consolidation center to several terminals, where packages are transferred to smaller trucks for last-mile deliveries. This paper formulates a model that solves the two-echelon delivery structure by the use of approximation techniques. Several potential terminal locations and demand areas were identified, and the optimal number and locations of the terminals were examined, as the model evaluated the most cost-effective routes between the consolidation center, potential terminals, and demand areas. Downtown Toronto, Ontario, Canada, was chosen as the case study area to assess the model, and a cost analysis of the number and locations of the terminals was performed. The experiments showed that the number and the locations of the terminals were greatly influenced by the opening cost of the terminals and the transportation cost of the delivery trucks. It was also discovered that the likelihood of selection of terminals that were positioned near both the consolidation center and the center of the service area was higher than the likelihood of selection of terminals at any other location.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".