Municipal solid waste supply chain optimization for value-added product development under uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optimizing municipal solid waste (MSW) management through the production of valuable products and energy conversion is crucial to mitigate environmental damage and promote economic sustainability. This study focuses on addressing the MSW supply chain problem by exploring the optimal location for the waste treatment. The supply chain network encompasses MSW transfer stations, treatment facilities, and markets with product demands. The methodological approach entails constructing a superstructure, gathering relevant data, and analyzing the results. Both deterministic MILP and two stage stochastic model are used in this study. A deterministic mixed-integer linear programming (MILP) model is employed to optimize the MSW supply chain problem, with the use of solver BARON. To account for uncertainties in supply–demand and transportation costs, a two-stage stochastic MILP model is developed. The deterministic equivalent approach is then employed to solve the stochastic model, resulting in an average solution across all scenarios. The decision variable pertaining to the selection of treatment technology locations is managed in the first stage. The second stage focuses on determining transportation and production-related decisions. Stochastic models can capture the inherent unpredictability of real-world systems by simulating a range of potential scenarios, helping to tackle uncertainty. To underscore the practical relevance of the mathematical programming formulation, a case study is presented and thoroughly analyzed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 it