Municipal solid waste supply chain optimization for value-added product development under uncertainty
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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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".