Development of a Fuzzy-Queue-Based Interval Linear Programming Model for Municipal Solid Waste Management
Bibliographic record
Abstract
In this study, a fuzzy-queue-based interval linear programming (FQ-ILP) model was first developed through introducing FQ model into an ILP framework. The FQ-ILP model can not only address system uncertainties with complex presentations, but also reflect the influence of FQ in decision-making problems. Moreover, it can be used for analyzing various policy scenarios that are associated with different waiting costs, fuzzy waiting times, and different operation costs. The method has been applied to a typical case study area for long-term municipal solid waste management planning. Interval solutions associated with fuzzy arrival rate, fuzzy service rate, and different waiting costs have been generated. They can be further used for generating decision alternatives and thus help waste managers to identify desired policies under various environmental, economic, and fuzzy queuing problems. Compared with the conventional optimization methods, the developed FQ-ILP model can more actually reflect the complexity of municipal solid waste management systems and provide more useful information for decision makers under uncertainty, resulting in increased system robustness. Results also suggest that the proposed method is applicable to other environment problems that involve uncertainties presented in multiple formats in the queuing models.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".