An Interval-Parameter Fuzzy-Stochastic Programming Approach for Air Quality Management under Uncertainty
Bibliographic record
Abstract
An interval-parameter fuzzy-stochastic programming (IPFSP) approach is developed for planning air quality management systems under uncertainty. Fuzzy sets theory is introduced to represent uncertainties existing in various operation costs under different loading conditions. Compared with the existing approaches, the proposed IPFSP performs uniqueness through two special features: one is it could provide more feasible control strategies under different upcoming pollutant amounts, which was seldom considered in the previous research efforts; the other is, as a result of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) being incorporated into the modeling framework, uncertain information expressed as discrete intervals and probability density functions can be effectively reflected. After formulating the model, a representative regional air quality management system is provided for demonstrating its applicability. The results indicate that reasonable solutions are obtained, and optimal management strategies with minimized system operation cost are generated for facilitating decision-making. Of more importance, the developed approach presents high efficiency in handling complex dissatisfactory data availability and enhancing system flexibility.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".