Inexact Minimax Regret Integer Programming for Long-Term Planning of Municipal Solid Waste Management—Part B: Application
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Bibliographic record
Abstract
In this study, an inexact minimax regret mixed integer programming (IMMRIP) method is applied to long-term planning of municipal solid waste (MSW) management in the City of Regina. The method can help tackle the dynamic, interactive, and uncertain characteristics of the solid waste management system in the city, and can address issues concerning plans for cost-effective waste diversion and landfill prolongation. Thirty-six situations were examined based on multiple alternatives and scenarios under different waste-generation levels. Reasonable solutions have been generated for decisions of system-capacity expansion and waste-flow allocation, demonstrating complex tradeoffs among system cost, regret level, and constraint-violation risk. Solutions associated with further inexact minimax regret (IMMR) analyses can help tackle tradeoffs between minimized system cost and maximized system feasibility. Under the optimal alternative, the system would reach a maximum reliability with the lowest risks of penalty and wastage. Results provide valuable inputs for adjustment of the existing waste flow allocation patterns to satisfy the city's diversion goals, long-term capacity planning for the city's waste management system, and generation desired policies for managing the city's waste collection and treatment.
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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