A sustainable facility location problem with vehicle allocation: an urban logistics case study
Bibliographic record
Abstract
Redesigning urban distribution networks contributes to reducing the negative impacts on the environment and people’s well-being caused by urban freight delivery operations. One solution is to locate a set of hubs in strategic sites in the city to make the distribution more efficient and environmentally friendly. The objective of this paper is to study the hub location problem and vehicle allocation, and to design a distribution network for freight deliveries in urban areas. Data from a case study in the city of Bogota, Colombia is used as an example of the proposed solution. Firstly, a mathematical programming model is proposed to minimize transportation costs, hub location costs, and CO2 emissions. Given the multi-objective nature of the problem, the solution is obtained following a lexicographic approach. Moreover, multiple scenarios are considered to analyse which might be the most suitable according to the decision-makers’ objectives or requirements. The results obtained through the proposed approach provide a tool for decision-makers to be strategic when choosing the solutions to implement for the case study. These insights and the modelling and solution approach can be generalized for implementation in other cities with similar concerns regarding urban freight distribution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".