Long-Term Planning of an Integrated Solid Waste Management System under Uncertainty—I. Model Development
Bibliographic record
Abstract
In the planning of an integrated solid waste management (ISWM) system, not only complicated interactions among various system components but also uncertain properties of many parameters and their interrelationships need to be considered. In this study, an inexact mixed integer linear programming model for long-term planning of the ISWM system is developed. The model can effectively reflect the complexities and uncertainties of the waste management system, as well as policies of waste diversion to extend useful lives of existing landfills. Economically, the model considers costs related to waste collection, transfer, transportation, processing and disposal, capital investments for developing and expanding waste management facilities, and revenues from recycled materials, finished compost, and residual facility values. Its solutions provide bases for answering questions of siting, timing, and sizing for new and expanded waste management facilities in relation to a variety of waste-diversion targets. Another advantage of the proposed model is that variations of system performance and decision variables can be investigated by solving relatively simple submodels, which makes it applicable to large-scale problems. Decision alternatives can be generated by adjusting values of the variables within the resultant intervals according to projected applicable conditions. Provision of these alternatives will allow decision makers to conveniently review and compare a number of potential schemes and make appropriate adjustment (within the resultant intervals) when necessary. In a companion paper, application of the developed model to a real case study in the City of Regina, Canada, will be reported. Details concerning applicability of the developed model, interpretation of the modeling outputs, and postoptimality analysis for the study system will also be explicated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".