Centralized production planning using reference operating points: application to fossil fuel power plants
Bibliographic record
Abstract
This article proposes a centralized Data Envelopment Analysis (DEA) approach for determining efficient operation points for the different plants of an organization given the desired aggregate production targets. The proposed approach minimizes the total input consumption and undesirable output generation. The concept of a reference operating point for each plant is introduced and used to scalarize the multiobjective problem as well as to anchor the targets computed for each plant. Additional DEA models to check the feasibility of the aggregate production targets and to gauge remaining slack capacity for each plant are also formulated. The proposed approach has been applied to the electricity mix and pollutant emissions of fossil fuel power plants owned by a large US utility. A scenario of 5% reduction in the aggregate electricity production has been considered together with +/−20% bounds on the total electricity produced by each plant. The results indicate that, giving the same importance to all pollutants, reductions of 6% and 9% for CO2 and Hg, respectively, and above 35% for SO2 and NOx can be achieved. These emissions reductions obtained by centralized production planning are larger than those that can be achieved by the individual plants independently determining their own production plans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".