Two-stage hybrid model for supplier selection and order allocation considering cyber risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the context of collaborative manufacturing, cyber risk caused by cyber attacks may lead to severe supply chain disruption. Currently, supplier selection and order allocation is regarded as effective means to mitigate the risks that might cause disruption. Thus, we propose a two-stage hybrid model for supplier selection and order allocation under cyber risk. The hybrid model consists of fuzzy analytical hierarchy process (Fuzzy AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and two-stage mixed integer linear programming (MIP). Based on the extracted cyber risk indicators, a Fuzzy AHP is used to calculate the level of cyber risk of suppliers. TOPSIS is utilized to quantitatively evaluate the cyber risk of suppliers and determine the ranking of suppliers. Then, a two-stage MIP model is developed to support decision-making on order allocation. The first-stage decisions are determined without emergencies, and the second-stage decisions are determined under emergencies. The results reveal that application of the proposed two-stage hybrid model could mitigate the negative impacts of cyber risks. By providing a theoretical basis and quantitative method for cyber risk evaluation, this research is of theoretical and practical significance to the field of supply chain management.
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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 | 0.000 |
| 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 | 0.001 | 0.004 |
| 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