Global Optimization Algorithm for Large-Scale Refinery Planning Models with Bilinear Terms
Bibliographic record
Abstract
We propose a global optimization algorithm for mixed-integer nonlinear programming (MINLP) problems arising from oil refinery planning. It relies on tight mixed-integer linear programming (MILP) relaxations that discretize the bilinear terms dynamically using either piecewise McCormick (PMCR) or normalized multiparametric disaggregation (NMDT). Tight relaxations help finding a feasible solution of the original problem via a local nonlinear solver, with the novelty being the generation of multiple starting points from CPLEX’s solution pool and the parallel execution. We show that optimality-based bound tightening (OBBT) is essential for large-scale problems, even though it is computationally expensive. To reduce execution times, OBBT is implemented in parallel. The results for a refinery case study, featuring units with alternative operating modes, intermediate storage tanks, and single- and multiple-period supply and demand scenarios, show that the algorithm’s performance is comparable to commercial solvers BARON and ANTIGONE.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".