Crude-Oil Operations under Uncertainty: A Continuous-Time Rescheduling Framework and a Simulation Environment for Validation
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a novel rescheduling framework of the crude-oil operations based on a continuous-time representation. Abnormal events and uncertainties in the crude-oil tank farm area are considered and analyzed in this framework to improve the robustness of the final plan. A rescheduling model is proposed to handle the various uncertainties. Some managerial experiences and consistency rules can be set in the model for different needs and scenarios. A multiagent based simulator is developed as the validation part with uncertainties to simulate the real-world operations and test the optimization results. The goal of the rescheduling framework is providing a feasible and flexible robust plan and dealing with the disruptive events and uncertainties at the same time. The results of the case studies indicate our framework can support dynamic optimization of crude-oil operations under complex real-world environments.
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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 | 0.001 |
| 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