Solutions selection based on the <scp>P</scp> ‐graph integrated data envelopment analysis for material scheduling in the ethylene production
Bibliographic record
Abstract
Abstract Material scheduling is significant in the ethylene production process. However, factors such as the production scale and the technology also affect the choice of material scheduling solutions. Therefore, this paper proposes a novel P‐graph methodology integrated data envelopment analysis (DEA) for the solutions selection of material scheduling. First, based on the basic procedure, a scheduling superstructure model expanding from the time dimension is built. With the cost as the objective function, the optimal and partially feasible scheduling solutions are generated based on the P‐graph, and the results are analyzed in detail with reference to ISA‐95. Then the DEA is used to analyze the indicators of material consumption and product yields to give a selection strategy from the input‐output perspective. The optimal solution and suboptimal solution sets are evaluated and analyzed, and decision making units (DMUs) with the highest score are calculated. Based on the scheduling solution with the highest score and relatively low cost, the potential adjustment of other solution is also given. Finally, a complete base of material scheduling solution has been implemented, which provides more references for decision makers. This paper considers the lowest cost as the goal and gives a more suitable alternative from the perspective of input‐output efficiency in combination with the actual production conditions, which is a good extension of the P‐graph.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".