A flexible closed loop supply chain design considering multi-stage manufacturing and queuing based inventory optimization
Why this work is in the frame
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Bibliographic record
Abstract
In this study, a multi-objective nonlinear model is used to design a closed-loop supply chain network. We consider a multi-period, multi-product and multi-stage manufacturing model by employing a queuing system for inventory management of finished and return products under demand uncertainty limitations. The purpose of this paper is to simultaneously reduce the waiting times of queues in warehouses and the costs of production, transportation, inventory and queuing systems. The flexibility of the chain is formulated at three concepts: product flexibility, the flexibility of supply against demand fluctuations, and the flexibility of time to satisfy all demand. The proposed non-linear model is solved by a robust genetic algorithm. A sensitivity analysis of the flexibility ratios reveals several insights for practitioners and academics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it