A PRACTICAL SCHEDULING APPROACH FOR THE STEEL-MAKING PROCESS
Bibliographic record
Abstract
This thesis presents a review of optimal production scheduling in the steel industry. Steel production encompasses various processes, such as the ‘’Blast Furnace’’ and ‘’Hot-Rolled Steel Sheets Mill’’. However, this thesis specifically focuses on the steel making process due to its intermediate nature and substantial influence on profits and costs. \\\\This paper presents a MILP scheduling method to tackle practical steel scheduling problems. The scheduling of steel process poses a significant challenge due to complicated constraints and machine rules, making it a time-consuming task to obtain an optimal solution. To address this problem, a strategy has been proposed to break down the huge and complex problem into smaller sub-problems. The foundational concept behind this approach was initially introduced by Harjunkoski and Grossmann (2001). However, further improvements are proposed in this thesis by introducing a more flexible model for process and grading selections, tailored to practical steel scheduling problems. The thesis presents a strategy to obtain optimal steel making process scheduling by using a MILP approach. In addition, this thesis shows an optimal steel making process scheduling under processing time uncertainty. Uncertain processing time can have great impact the schedule accuracy. To tackle with this problem, a stochastic scheduling model is represented. Moreover, this thesis illustrates an improvement to generate a practical scheduling of steel making process by making use of real processing time data. To validate the effectiveness of our proposed methods, we provide a small example for each step of the scheduling process. The results demonstrate that our approach yields reasonable scheduling solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".