A Fast and Accurate Algorithm for Stochastic Integer Programming, Applied to Stochastic Shift Scheduling
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Bibliographic record
Abstract
Stochastic programming can yield significant savings over deterministic approaches. For example, the stochastic approach for the shift scheduling problem solved in [6] yields more than 15% savings on some instances. However, stochastic approaches always lead to very large problems (around 10 million IP variables in [6]), since a recourse must be computed for every scenario. There is no fast and exact method for solving such problems. In this article, the algorithm presented in [6] is improved in two ways: a Benders cuts dynamic management algorithm for the master problem and a multithreaded implementation to solve the subproblems. Those two improvements yield a heuristic able to solve a 10 million variables IP problem in less than 5 minutes, with a very good accuracy, and enables the resolution of larger instances. This algorithm uses general ideas that can easily be adapted to every problem that, in order to be solved, is split into a master problem and several subproblems: L-shaped method, column generation. . . Les Cahiers du GERAD G–2012–29 1
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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