Environmental Management Under Uncertainty—An Internal-Parameter Two-Stage Chance-Constrained Mixed Integer Linear Programming Method
Bibliographic record
Abstract
In this study, an internal-parameter two-stage chance-constrained mixed integer linear programming (ITCILP) method is developed for municipal solid waste (MSW) management under uncertainty. The ITCILP improves upon the existing optimization methods with advantages in uncertainty reflection, policy investigation, and risk analysis. It can directly handle uncertainties presented as both internals and probability density distributions, and can thus support the assessment of the reliability of satisfying (or the risk of violating) various constraints, for accomplishing a minimizing system cost. It can also be used for analyzing various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. Moreover, within a multistage context, the ITCILP can facilitate dynamic analysis for capacity-expansion planning under different constraint-violation risk levels. The developed method is applied to a case study of long-term MSW management planning. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different levels of constraint-violation risk. They demonstrate the practical applicability of the developed methodology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".