Identification of Optimal Urban Solid Waste Flow Schemes under Impacts of Energy Prices
Bibliographic record
Abstract
A large number of interval linear programming models were developed for urban solid waste management under uncertainty. Nevertheless, few of them can address the possible effects of energy prices on the identified waste management policies. This study proposes a fuzzy interval semi-infinite programming model by introducing the concept of a functional interval into the waste management problem. The model is applied to a case study and compared to the existing fuzzy interval linear programming (FILP) and interval semi-infinite programming (ISIP) models to illustrate the differences in producing associated waste management polices. Results reveal that the energy prices do have impacts on the optimal waste flow patterns, net system benefit, and satisfactory degree. In contrast to FILP, the FISIP model can produce a policy with low risk of system failure (or high satisfactory degree), and has better adaptability to the energy price fluctuations. In addition, the FISIP model has the advantages over the ISIP model in that it can (1) simultaneously deal with multiple types of parameter uncertainties, (2) allow for the existence of tolerance intervals for each of the constraints, (3) reflect what extent the constrains would not be violated, and (4) provide flexible patterns for cost-effectively transporting the urban solid waste.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".