A bi-objective location routing optimization with fuzzy time-dependent societal risks for enhancing urban medical waste management system
Bibliographic record
Abstract
This paper aims to enhance medical waste management by adding new recycling centers or upgrading existing facilities, properly planning vehicle acquisition and routing under the consideration of both societal and economic impacts. Specifically, we focus on the threats posed to the surrounding population during collection and recycling by formulating a fuzzy time-dependent societal risk assessment that integrates the exposure distance estimated in terms of fuzzy vehicle speed into the traditional risk model. Then, considering multiple types of medical wastes and compatible vehicles, a bi-objective location-routing model is developed to make location-routing decisions simultaneously minimizing societal risk and system cost. The complexity of the resulting mathematical model motivates the adoption of three multi-objective optimization approaches, which are used to test our proposed model using a real-life network in Shenzhen, China. This research suggests an affordable opportunity to upgrade the current waste management system to align with the post-pandemic “new normal” by adapting existing facilities for medical waste recycling. The proposed risk measure results in better-controlled total and transportation risks, as well as a more equitable distribution of risk. Compared to the current policy, our recommended plan can reduce the system risk by more than 50% with only a 22% increase in cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".