Multistage adaptive stochastic mixed integer optimization under endogenous and exogenous uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Abstract To solve multistage adaptive stochastic optimization problems under both endogenous and exogenous uncertainty, a novel solution framework based on robust optimization technique is proposed. The endogenous uncertainty is modeled as scenarios based on an uncertainty set partitioning method. For each scenario, the adaptive binary decision is assumed constant and the continuous variable is approximated by a function linearly dependent on endogenous uncertain parameters. The exogenous uncertainty is modeled using lifting methods. The adaptive decisions are approximated using affine functions of the lifted uncertain parameters. In order to demonstrate the applicability of the proposed framework, a number of numerical examples of different complexity are studied and a case study for infrastructure and production planning of shale gas field development are presented. The results show that the proposed framework can effectively solve multistage adaptive stochastic optimization problems under both types of uncertainty.
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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