Optimization of Classification Track Assignment Considering Block Sequence at Train Marshaling Yard
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Bibliographic record
Abstract
An operational process at train marshaling yard is considered in this study. The inbound trains are decoupled and disassembled into individual railcars, which are then moved to a series of classification tracks, forming outbound trains after being assembled and coupled. We focus on the allocation plan of the classification tracks. Given are the disassembling and assembling sequence, the railcars connection plan, and a number of classification tracks. Output is the assignment of the railcars to the classification tracks. An integer programming model is proposed, aimed at reducing the number of coupling operations, as well as the number of dirty tracks which is related to the rehumping operation, and the order of the railcars on the outbound train must satisfy the block sequence. Tabu algorithm is designed to solve the problem, and the model is also tested by CPLEX in comparison. A numerical experiment based on a real-world case is analyzed, and the result can be reached within a reasonable amount of time. We also discussed a number of factors that may affect the track assignment and gave suggestions for the real-world case.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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