A bi-objective data-driven chance-constrained optimization for sustainable urban medical waste management
Bibliographic record
Abstract
The processing and transportation of medical waste pose uncertain threats to the surrounding people and the environment in urban road networks. This paper aims to mitigate such risks under an emergency system with uncertain response times. In more detail, we first formulate an integrated pollution-population risk assessment that estimates the dynamic impact on the exposed population by embedding the emergency response time into the risk measure. Given the variability in traffic conditions, the response time is uncertain, which also affects the associated risks. Taking this randomness into consideration, a bi-objective chance-constrained model is developed to seek optimal facility locations, vehicle acquisitions, as well as route and tour plans, such that both the risk and cost are simultaneously minimized. To meet practical restrictions on medical waste collection, continuously accumulative vehicle load and volume constraints are added to the two-commodity flow formulation. Then, we propose a comprehensive solution procedure that integrates a Back Propagation Neural Network approach within the fuzzy chance constraint framework to address uncertainties. Two multi-objective methods, an augmented ɛ -constraint solution technique and a nearest-neighbor Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, are implemented respectively for small- and large-scale problem instances. A series of numerical experiments are conducted on a real-life situation in Shanghai city of China to demonstrate the workability of the proposed model and approach. The numerical results show that our recommended system can effectively prevent the overall capacity shortage, reduce the total cost and risk respectively by more than 8% and 11%, as well as lower the transportation risk and distance respectively by nearly 15% and 23%. • Urban medical waste management is studied under uncertain emergency response time. • An integrated pollution-population measure is proposed to estimate dynamic risks. • Vehicle load and volume constraints are improved in two-commodity flow formulation. • BPNN is integrated with fuzzy chance constraints to model uncertainties. • A nearest-neighbor NSGA-II algorithm is proposed for solving large instances.
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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 | 0.002 |
| 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) | 0.003 | 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".