Interval stochastic quadratic programming approach for municipal solid waste management
Bibliographic record
Abstract
In this study, an interval stochastic quadratic programming method (ISQP) is developed through incorporating techniques of chance-constrained programming (CCP) and inexact quadratic programming (IQP) within a general framework. This method improves upon the conventional IQP approaches in uncertainty reflection and risk analysis. Interval stochastic quadratic programming can handle dual uncertainties expressed as interval values and probability distributions, and can deal with nonlinearities in objective function to reflect economies-of-scale effects on the system cost. It can also support the assessment of the risk of violating various constraints, for accomplishing a minimizing system cost. The developed ISQP is applied to a municipal solid waste (MSW) management system with multiple disposal facilities and multiple cities within multiple time periods. Results of the case study indicate that useful solutions for planning MSW management practices have been generated under different probability levels of violating constraints, which are informative and flexible for decision makers. A high system cost is associated with a low risk level of violating constraints, and a low system costs will run into a high probability of violating constraints. There is a tradeoff between the system cost and the constraint-violation risk.
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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 | 0.002 |
| 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) | 0.002 | 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".