Tactical and Operational Planning of Multirefinery Networks under Uncertainty: An Iterative Integration Approach
Why this work is in the frame
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Bibliographic record
Abstract
The oil industry is increasingly interested in improving the planning of their operations, because of the dynamic nature of the oil business. This study intends to establish an iterative integration approach for the tactical and operational planning of multisite refining networks. Tactical and operational mathematical models are proposed. Both models are two-stage stochastic linear programs in which uncertainty is incorporated into the dominant random parameters at each decision level. Decisions made in the oil industry differ based on multisite network echelon (spatial integration) and planning horizon (temporal integration). Spatial integration is discussed at the tactical level, whereas temporal integration is discussed with respect to the interaction between the two levels. In the proposed temporal integration approach (iterative approach), there is a cyclic information flow between the two models. An industrial scale study using data from the Brazilian oil industry was conducted to discuss the benefits of integration in a stochastic environment.
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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 | 0.001 |
| 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