Strategic Planning of Integrated Multirefinery Networks: A Robust Optimization Approach Based on the Degree of Conservatism
Bibliographic record
Abstract
This paper considers the problem of strategic planning under uncertainty for optimal integration and coordination of a multirefinery network. The deterministic model proposed by Al-Qahtani and Elkamel [ Comput. Chem. Eng. 2008, 32, 2189−202] was extended to account for uncertainties in raw material costs and final product prices, as well as product demand. The robust optimization methodology of Bertsimas and Sim [ Op. Res. 2004, 52, 35−53] was applied, which deals with uncertainty in a tractable manner and does not add complexity to the deterministic problem. An industrial-scale study illustrated the benefits of the integrated planning and demonstrated that the modeling of uncertainty in process parameters provides a more practical perspective of the refining industry. In addition, probability bounds of constraint violation were calculated to help decision makers select appropriate parameters to control solution robustness and evaluate trade-offs between conservatism and total profit.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".