Capacity Scalability in Robust Design of Supply Flow Subject to Disruptions
Why this work is in the frame
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Bibliographic record
Abstract
Within the last decade, several cases of the supply chain vulnerability to major disruptions have been observed. The typical mitigation strategies such as safety inventory and excess capacity are inadequate to cover the major disruptions. Furthermore it is not economical to invest in such costly proactive strategies to recover from infrequent disruptions. The objective of this paper is to provide a decision making tool achieving robust supply flow by incorporating strategic stock and reconfigurable back-up supplier in mitigating disruptions. We consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive due to re-configurability characteristics. We present a multi-stage robust optimization model to determine optimal strategic stock levels and layout configuration of the back-up supplier for a supply chain subject to random realization of disruptions and available capacity during the ramp-up time. Furthermore, the partial available capacity of the backup supplier during the response time has been modelled using queuing theoretical models. The results show the optimality of highly scalable configuration as the decision maker becomes more risk averse.
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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 | 0.001 |
| 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 | 0.001 |
| 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