Locating on-street loading and unloading spaces by means of mixed integer programming
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Bibliographic record
Abstract
In an urban freight distribution system, determination of the number and location of loading-unloading places is required to regulate loading-unloading operations. This paper presents mathematical models for on-street loading-unloading space location based on set-covering problem and p-median problem formulations. The approaches was tested with real data: an area has 160 city blocks and 60 on-street loading-unloading spaces, in Fortaleza, Brazil. We evaluated four scenarios considering different radius of influence of a loading/unloading spaces. The results indicate this approach has potential for achieving gains in terms of reduction of the distance between the clients and the loading and unloading places: considering that the average distance is a performance indicator (ratio between the total distance and the covered clients), a radius of influence of 400 meters has best relation (0.489) and all clients are covered. The results indicate that the model can be used by planners to allocate loading and unloading areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it