An analytical approach to designing a circular waste management system
Bibliographic record
Abstract
The increasing generation of medical waste, driven by higher consumption levels, changing lifestyles, and natural disasters, threatens both the environment and human health. Medical waste from healthcare centers is particularly concerning due to its hazardous nature, necessitating effective management strategies. This study addresses key challenges in Medical Waste Management Systems (MWMS), including fluctuating waste generation, diverse waste types, incompatible handling practices, container and truck management, and the need for sustainable circular waste management. To tackle these issues, we developed a two-stage Stochastic Mixed-Integer Linear Programming (MILP) model to optimize MWMS network design. The model incorporates revenue generation from recycling, Waste-to-Energy (WTE) conversion, and container reuse while minimizing costs and environmental impacts. The model’s robustness is enhanced through data-driven parameter estimation, treatment technology selection, and revenue forecasting. To efficiently address the computational complexities associated with large-scale stochastic optimization, we employed a combination of the Sample Average Approximation (SAA) technique and a novel Hybrid algorithm that integrates deterministic optimization with metaheuristic methods, enhancing solution robustness and scalability. The model’s efficacy was validated through a case study in Hamilton, Ontario, Canada, where results demonstrated a 90.5 % reduction in computational time and a 56.7 % reduction in binary variables compared to the original model. The optimized solution achieved an annual waste disposal capacity of 300,000 tons, with an average revenue of $55 million, including $24.1 million from waste disposal, $17.1 million from recycled products and electricity, and $12.3 million from container reuse. Additionally, the network design reduced operational costs to $29.6 million and transportation costs to $6.4 million. This research contributes to the field by addressing gaps related to waste-to-container compatibility, revenue generation from reused materials, and uncertainty management. Future work may focus on enhancing predictive models for waste generation, integrating real-time data analytics, and expanding the framework to other regions with diverse waste management challenges.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".