Dual-Interval Linear Programming Model and Its Application to Solid Waste Management Planning
Bibliographic record
Abstract
A dual-interval parameter linear programming (DILP) model was developed and applied to the planning of municipal solid waste (MSW) management systems under uncertainty. The DILP can address system uncertainties with complex presentations. Parameters in the DILP can be expressed as interval numbers; however, due to the complexity of the real world, highly uncertain information may exist in the boundaries of interval parameters. When sufficient information for these boundaries is available to access intervals, the novel concept of dual interval (being an interval-boundary interval) can be developed for handling such uncertainties and be introduced into the existing interval-parameter linear programming (ILP) framework; this leads to the DILP method where the uncertain parameters are represented as single or dual intervals. The DILP approach improves upon the ILP method by allowing dual uncertainties (presented as dual intervals) to be incorporated into the optimization processes. Decision alternatives can be generated through analysis of the single- and dual-interval solutions according to projected applicable conditions. Applicability of the developed model was demonstrated through a case of long-term MSW management planning. Reasonable solutions can be obtained, which are useful for generating desired decision alternatives and providing more information to decision makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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".