Design of reverse logistics networks under uncertainty: Multi-objective approach
Bibliographic record
Abstract
A reverse logistics network (RLN) is defined as the backward flow of products, specifically the products that are returned for recycling. Several entities are involved in a recycling process such as regional collection depots, recovery centers, remanufacturing plants, and disposal centers. The main objective of RLN design is to facilitate the reclamation of used products for the purpose of saving cost, energy, resources, and diverting waste from landfills and waterways. On this matter, decision-makers should consider different types of parameters (i.e., fixed and variable costs, the quantity of demand and return, and the quality of returned products) affecting the configuration of facility location models. In real life, there are a variety of ambiguities associated with mentioned parameters that stem from either internal or external factors (e.g., volatility in market demand, rate of the returned products, unit transportation cost). The main objective of this dissertation is to develop multi- objective optimization models under uncertainty. In this regard, some integrated solution methodologies are introduced to address different types of uncertainty in five stewardship programs (i.e., electronic recycling association (ERA), Canadian battery association (CBA), beverage container stewardship program regulation (BCSPR), Ontario electronic stewardship (OES), wastewater management in hydraulic fracturing) in Canada. To consider the environmental impact of such stewardship programs, the proposed mathematical models are extended to the multi-objective optimization models. In this regard, the proposed solution methodologies make decision-makers capable of optimizing the environmental aspects (e.g., green practices of third parties, carbon emissions) associated with RLNs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".