A Constraint-Softened Interval-Fuzzy Linear Programming Approach for Environmental Management Under Uncertainty
Bibliographic record
Abstract
In this study, a constraint-softened interval-fuzzy linear programming (CS-IFLP) method is developed for violation analysis of environmental management systems under uncertainty. CS-IFLP can deal with uncertainties presented in terms of fuzzy sets and intervals. Moreover, a number of fuzzy relaxation levels for system constraints are allowed, such that the relevant decision space can be expanded. This can help generate a range of decision alternatives under various system conditions, and facilitate in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The developed method is applied to a case study of long-term municipal solid waste management planning. Results indicate that reasonable solutions for both binary and continuous variables have been generated. A higher relaxation level could result in a lower system cost and a higher satisfaction degree, but with a higher constraint-violation risk. Results of the sensitivity analyses demonstrate that violated system constraints have various effects on the system cost and satisfaction degree.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".