An Integrated Decision-Making Framework for a Closed-Loop Supply Chain Network Redesign Problem
Why this work is in the frame
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Bibliographic record
Abstract
The rapid growth in demand and environmental concerns in industries like glass manufacturing necessitate the redesign of closed-loop supply chain (CLSC) networks to address both operational inefficiencies and sustainability challenges. Unlike conventional supply chain design, redesigning CLSC networks involves strategic decisions such as opening new facilities, closing existing ones, and managing the cost trade-offs associated with these transitions. Motivated by these challenges, this paper proposes an integrated decision-making framework to tackle the closed-loop supply chain network redesign (CLSCNR) problem. The proposed framework is formulated as a mixed-integer programming (MIP) model, specifically tailored for the glass industry. The forward supply chain includes suppliers, manufacturers, distributors, and customers, while the reverse supply chain comprises collection centers that allocate returned and waste products to recycling, remanufacturing, or disposal centers. This redesign approach addresses critical challenges in facility location, capacity planning, and customer assignment to better align supply chain operations with increasing demand and sustainability goals. Extensive numerical analyses were conducted using 16 test instances, revealing significant improvements through network redesign. For example, the number of open centers decreased by 1 in several instances (such as T5 and T9), while in other instances, up to 3 centers were closed (e.g., T13). The difference in the number of open centers before and after the redesign highlights the ability of the proposed framework to streamline network operations while maintaining service levels. The computational time ranged from 27.48 seconds for smaller instances to 62.26 seconds for larger ones, demonstrating the model's efficiency and scalability. The findings demonstrate the proposed MIP's ability to optimize network configurations, enhancing operational efficiency and demand satisfaction. These insights provide a practical decision-support tool for supply chain designers, enabling companies in high-demand industries to achieve adaptive and sustainable CLSC networks.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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