Fuzzy Inexact Mixed-Integer Semiinfinite Programming for Municipal Solid Waste Management Planning
Bibliographic record
Abstract
Based on the concept of functional intervals, fuzzy inexact mixed-integer semiinfinite programming (FIMISIP) method is developed for municipal solid waste management planning. The method allows the uncertainties in parameters expressed as fuzzy, interval, and functional interval numbers to be directly communicated into the programming problem. The FIMISIP problem is solved by dividing it into two interactive semiinfinite programming (SIP) subproblems. Solutions reflecting the inherent uncertainties can then be generated by combining the SIP solutions into a set of decision intervals. The method is applied to a municipal solid waste management planning system for demonstrating its effectiveness in dealing with uncertain and dynamic complexities. Compared to the previous inexact programming methods, FIMISIP has the advantages as follows: (1) the dynamic complexity can be addressed by introducing the functional-interval parameters associated with time into the programming problem; (2) the FIMISIP solutions provide a set of flexible waste-management schemes to the decision makers; and (3) the FIMISIP solutions are more reliable than those from the previous ILP ones since they can be “really” optimal regardless of how the parameters vary with time within the time period. While this study is a first attempt to solve solid waste management issues under complex uncertainties, the method can be extended to other environmental management planning problems.
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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 | 0.000 | 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) | 0.001 | 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".