Lean Railroading for Improving Railroad Classification Terminal Performance
Bibliographic record
Abstract
Although much attention has been focused on the growth of intermodal traffic over the past decade, manifest freight (or carload) traffic is a major revenue generator for railroads. The high potential profitability of carload traffic suggests that railroads should try to grow this segment of traffic further, especially in an era of limited railway capacity. To do this, they must meet the increasing logistical needs of their customers by providing more reliable service. The classification terminal is a key determinant in service reliability of manifest freight. Terminal performance also affects network efficiency. Regression analysis showed that, as average dwell time increased, average manifest train speed decreased. Inadequate terminal capacity is viewed by many as a barrier to improved service reliability and network efficiency. Because terminals can be considered production systems, insight is gained by adapting tools that have led to significant performance improvement in manufacturing. A new approach is introduced: lean railroading. The most important manufacturing process analog to improving terminal capacity is the bottleneck. The train assembly (pull-down) process has been identified as the bottleneck in a majority of classification yards. A sensitivity analysis conducted on three bottleneck management alternatives suggests that pull-down capacity can be increased by as much as 26%, compared with the baseline case without large labor or capital expenses, through better management of the process and its interactions with the system. To maximize efficient use of rail yard infrastructure and resources, more emphasis should be placed on the quality of the classification process, rather than on quantity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".