Recoverable Robustness for Train Shunting Problems
Why this work is in the frame
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Bibliographic record
Abstract
Several attempts have been done in the literature in the last years in order to provide a formal definition of the notions of robustness and recoverability for optimization problems. Recently, a new model of recoverable robustness has been introduced in the context of railways optimization. The basic idea of recoverable robustness is to compute solutions that are robust against a limited set of disturbances and for a limited recovery capabilities. The quality of the robust solution is measured by its price of robustness that determines the trade-off between an optimal and a robust solution. In this paper, within the recoverable robustness model, we emphasize algorithmic aspects and provide definitions of robust algorithm and price of robustness of a robust algorithm as a measure to evaluate its performance. A robust algorithm provides a solution that maintains feasibility by possibly applying available recovery capabilities in the case of changes to the input data. We study various settings in the context of shunting problems, i.e. the reordering of train cars over a hump yard. The considered shunting problems can be seen as the reordering of an integer vector by means of a set of available stacks with the further constraint that the pull operation does not involve only the element on top of a stack, but all the elements contained in the stack. We provide efficient robust algorithms concerning specific shunting problems. In particular, we study algorithms able to cope with disturbances, as temporary and local unavailability and/or malfunctioning of key resources that can occur and affect planned operations. Various scenarios are considered, and robustness results are presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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