Robust Fully Fuzzy Programming with Fuzzy Set Ranking Method for Environmental Systems Planning Under Uncertainty
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Bibliographic record
Abstract
This article proposes a new robust interactive interval fully fuzzy linear programming (RIIFFLP) method, using the fuzzy ranking method to find a balance between the requirements of constraints and the objective function of a fuzzy set function, as a technique for optimal decision-making under uncertainty. It considerably improved previous interval fuzzy linear programming (FLP) methods by using a new solution method named the robust two-step method (RTSM). The RTSM has a higher membership degree for the fuzzy subset, which allows interval results to stay within the boundaries of constraints. This RIIFFLP model was applied to a case study in municipal solid waste management. Results demonstrated that the solution obtained from the RIIFFLP model had more feasible results by comparison with existing FLP methods. The RIIFFLP also provided optimal interval solutions in different α levels that made decision making easier by simply choosing a suitable scenario. Therefore, the RIIFFLP model can be considered a better practical method for problem-solving when uncertainties are present.
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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 | 0.001 |
| 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