An analytical approach to planning for and managing random disruption in rail intermodal networks
Bibliographic record
Abstract
Rail-truck intermodal transportation relies on intermodal trains for long-haul movement and trucks for highway delivery. It accounts for a significant portion of freight traffic in North America, making its reliable operation vital to the economy. However, these networks are vulnerable to disruptions, highlighting the need to ensure their resilience under such conditions. This article proposes two optimization models: the first addresses terminal location, train service design and freight routing under normal operations; the second incorporates disruptions at intermodal terminals with varying severity levels. To account for disruption uncertainty, we define disruption scenarios and employ a two-stage stochastic programming approach and introduce recovery strategies to maintain shipment continuity. In addition, we outline a solution heuristic for larger problem instances. The proposed optimization programs are applied to a case study based on the USA, and the resulting analyses highlight the importance of incorporating disruption especially given the impact on train services and routing of containers, highlight the role of terminal utilization under normal operating conditions on the decision to repair terminals under disruption and the impact on service legs, and reiterates the significance of designing train services to maintain a balanced offering and ensure connectedness.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".