Mathematical Modeling for Identifying Cost-Effective Policy of Municipal Solid Waste Management under Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
In municipal solid waste (MSW) management, many impact factors, such as waste generation rate, treatment capacity, diversion goal, and disposal cost appear uncertain. These uncertainties can result in difficulties in the long-term planning of MSW management activities. A critical issue that decision makers should mitigate is how to address these uncertainties due to a lack of knowledge founded on an incomplete characterization, understanding or measurement of MSW systems. In this study, an inexact twostage waste management (ITWM) model is developed for planning long-term MSW management in the City of Changchun, China. The ITWM model incorporates the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within an integer programming framework, such that uncertainties expressed as both intervals and probabilities can be reflected; it can also analyze different policy scenarios that are associated with different economic penalty levels. Two cases related to different waste management policies are examined, generating varied levels of waste-management cost and system-failure risk. The results obtained are valuable for addressing issues of waste diversion and capacity expansion with a minimized system cost. They also suggest that the developed model be meaningful for real-world planning problems and the practicality of this approach can be extended to other environmental planning applications containing significant sources of uncertainty.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it