A budget-constrained partial protection planning of a rail intermodal terminal network
Bibliographic record
Abstract
Abstract Rail-truck intermodal transportation is an essential component of freight transportation in North America, and thus, its associated infrastructure is deemed crucial for the wellbeing of the society. In this paper, a budget-constrained partial protection planning model is proposed that addresses the fortification of a rail intermodal network such that the effects of an intentional disruption are minimized. Novel solution methodologies that make use of metaheuristic approaches and a decomposition approach are proposed to solve the challenging tri-level mixed-integer mathematical model. The proposed analytical approaches are then used to solve and analyze problem instances generated using the realistic infrastructure of a major railroad operator. Finally, the computational efficiency and effectiveness of the proposed approaches over the existing solution techniques in the literature are discussed and future research directions are outlined. Article Highlights The paper suggests partial protection of facilities together with two metaheuristic approaches to solve the model. The proposed metaheuristic approaches are efficient in solving the model compared to the existing exact approach. Having the option to partially protect facilities provides effective use of scarce defensive resources.
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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 | 0.001 |
| 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 | 0.001 | 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".