Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach
Bibliographic record
Abstract
Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".