Optimization of the Shunting Operation Plan at Electric Multiple Units Depots
Bibliographic record
Abstract
The number of standard electric multiple units (EMUs) in China has increased from 1003 in 2013 to 3256 in 2018. For maintaining all EMUs in time, the high-speed rail system with the fast-developing number of EMUs is facing growing pressure. The maintenance and cleaning capacity of an EMU depot can be improved by a better shunting operation planning (SOP). This paper considers an SOP problem at EMU depots, which may have two types of yards, namely, stub-end and through. Every track at an EMU depot has two sections and can accommodate two short standard EMUs of 8 railcars or one long EMU of 16 railcars. As the SOP is currently handled manually by dispatchers, this paper proposes two integer linear programming models for two types of yards for daily planning and dispatching, which aim at minimizing the total delay time of all EMUs during the planning horizon. A Reduced Variable Neighborhood Search (RVNS) algorithm is designed to improve the solution efficiency. The results of the numerical experiment show that the RVNS algorithm can yield an optimal maintenance plan in a few seconds for depots of different layout types and can be applied to a computer-aided planning system. The track utilization rate of the maintenance yard with the stub-end type is higher than that of the through type. The stub-end type may be more suitable for the current schedule, as its total track utilization rate is much lower than the through type.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".