A Network Design Approach for Circular Medical Waste Management using Two-stage Stochastic MILP Optimization
Bibliographic record
Abstract
The escalating production of medical waste, attributed to increased consumption levels, changing lifestyles, and natural disasters, poses a threat to the environment and human health. Among the hazardous waste categories, medical waste from healthcare centers stands out as a significant concern, demanding effective management strategies. This research addresses the challenges of fluctuating and uncertain waste generation, diverse waste types, incompatible handling practices, container and truck management, high costs, and the imperative for a circular waste management approach in the Medical Waste Management System (MWMS). A two-stage Stochastic Mixed-Integer Linear Programming (MILP) model is proposed to optimize the network while considering revenue generation from recycling, Waste-to-Energy (WTE) conversion, and reusing practices. The model's efficacy is demonstrated through a detailed case study implemented in Hamilton, Ontario, Canada. Data-driven parameter estimation, treatment technology selection, and revenue estimation contribute to the robustness of the model. The use of the Sample Average Approximation (SAA) technique efficiently handles the computational complexities of the two-stage stochastic MILP model, making it applicable to large-scale problems. Moreover, the research showcases the data analysis undertaken to estimate parameters for the case study, providing valuable insights for effective waste management strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".