A Tactical Planning Model for Railroad Transportation of Dangerous Goods
Why this work is in the frame
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Bibliographic record
Abstract
Railroad transportation of hazardous materials did not receive as much attention as highway transportation in the academic literature, although comparable volumes are shipped via these two transport modes in North America and Europe. In this paper, we present an optimization methodology for the railroad tactical planning problem with risk and cost objectives. We determine the routes to be used for each shipment, the yard activities, and the number of trains of different types needed in the network. The transport risk assessment component of our model incorporates the differentiating characteristics of railroad operations. We develop a memetic algorithm-based solution methodology, which combines genetic and local searches, to solve the biobjective model. The railroad infrastructure in the midwestern United States is used as a basis for generating problem instances of the size encountered in real life. Our analyses of the solutions of instances indicate that it is possible to achieve significant reductions in population exposure without incurring unacceptable increases in operational costs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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