Mathematical Modeling for Identifying Cost-Effective Policy of Municipal Solid Waste Management under Uncertainty
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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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".