Balanced Uncertainty Sets for Closed-Loop Supply Chain Design: A Data-Driven Robust Optimization Framework with Fairness Considerations
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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".