An inexact stochastic quadratic programming method for municipal solid waste management
Bibliographic record
Abstract
The existences of nonlinearities and uncertainties are the main complexities that cause difficulties in planning municipal solid waste-management systems. In this study, an inexact stochastic quadratic programming method is developed for handling nonlinearities in the cost objective to reflect the economies of scale and uncertainties expressed as probability distributions and discrete intervals. This model improves upon the conventional inexact quadratic programming and two-stage stochastic programming approaches. It can better reflect system cost variations and generate more reasonable and applicable solutions. It can also be used for analysing various policy scenarios that are associated with different levels of penalties when the promised policy targets are violated. The developed method is applied to a case of long-term waste-management planning. The interactive and derivative algorithms are employed for solving the developed model. The solutions are presented as combinations of deterministic, interval and distributional information. They can be used for generating decision alternatives and thus help waste managers to identify desired policies under various environmental, economic and system-reliability constraints.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".