Economic feasibility analysis of a medical mask closed-loop supply chain: A case study in the Montreal region
Bibliographic record
Abstract
The world was exposed to a global health crisis due to covid-19. This situation generated an unprecedented increase in the use of single-use medical materials, notably medical face masks. This study focuses on the design and planning of a closed-loop supply chain (SC) for dealing with end-of-life medical face masks. An optimization model to efficiently collect and recycle used medical face masks is proposed. The main benefits are the correct disposal of contaminated products and component recycling. The considered SC network includes suppliers of virgin raw materials, medical face mask manufacturing centers, warehouses, distribution centers, business clients, collection and recycling centers, and finally, clients for the recycled components. Decisions to be made include material flows in the network, supplier (collection and recycling centers) selection in order to maximize the profit of the SC. A realistic case study is created based on real data gathered from different industrial partners in the Montreal region and surrounding areas. Various scenarios are analyzed to identify the conditions under which the SC is profitable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".