Optimization of regional waste management systems based on inexact semi-infinite programming
Bibliographic record
Abstract
This study proposes an inexact semi-infinite programming (ISIP) method for dealing with engineering optimization problems. The ISIP problem is solved by dividing it into two interactive linear programming subproblems and then solved by conventional simplex method, respectively. The method is applied to a system for identifying optimal regional waste management strategies under uncertainty. The results indicate that the generated strategies obtained though ISIP would not increase the complexity in decision-making processes. Compared to interval linear programming (ILP), ISIP has the advantages of (i) better reflecting the association of the total system revenue with gas and power prices, (ii) generating more reliable solutions with a lower risk of system failure due to the possible constraints violation, and (iii) providing a more flexible management strategy since the capital availability can be adjusted with the variations in gas prices. Although only a hypothetical but representative system is applied in this study, the proposed ISIP method may be applicable to many other systems where the complex uncertainties in parameters should be taken into account.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".