Balanced Uncertainty Sets for Closed-Loop Supply Chain Design: A Data-Driven Robust Optimization Framework with Fairness Considerations
Why this work is in the frame
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Bibliographic record
Abstract
Recycling is crucial for minimizing the environmental impact of plastic waste and plays a key role in sustainable supply chains. However, optimizing these networks is challenging due to uncertainties in parameters such as demand and return rates. This research focuses on designing robust Closed-Loop Supply Chain (CLSC) networks to enhance resource efficiency, reduce costs, and increase recycling rates, making the system more resilient and sustainable. A novel Data-Driven Robust Optimization (DDRO) approach is developed, incorporating historical data to manage uncertainties, such as fluctuating demand and return rates. The research introduces the concept of “balanced uncertainty sets,” where boundaries are equidistant from uncovered data, ensuring a fair and true representation of uncertainty. A Kernel Weight Adjustment (KWA) approach is proposed to balance uncertainty sets across varying levels of conservatism. Additionally, a Robust Fairness (RF) index is proposed to evaluate the balance of uncertainty sets, and a data-driven algorithm is developed to compute the RF index efficiently. Numerical results show that the developed DDRO approach generates more balanced uncertainty sets, and the RF index allows for effective comparison without added computational complexity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it