Analyzing the barriers to resilience supply chain adoption in the food industry using hybrid interval-valued fermatean fuzzy PROMETHEE-II model
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
The resilient food supply chain (RFSC) has been identified as an effective model for mitigating food supply chain (FSC) risks. However, there exist many barriers impacting the implementation of the RFSC. Further, previous studies seldom utilize integrated decision models for identifying and ranking the barriers to implementing RFSC within uncertain environments. Thus, the study establishes an interval-valued Fermatean fuzzy (IVFF) decision framework to identify and rank these barriers. The framework is classified into four stages. First, to model the interaction between preference information, we introduce the IVFF-prioritized weighted average (PWA) operator to collect this information. Then, an integrated IVFF-CRITIC method is proposed to calculate the barrier weights considering their inter-correlation relationships. Next, the IVFF-PWA operator and IVFF-CRITIC method are incorporated into the PROMETHEE-II model to rank the barrier levels of alternative participation in the FSC. Further, a case study about analyzing implementation barriers to RFSC is employed to test the effectiveness and practicality of the presented framework. The result shows that the participation food processing company (priority: 0.161) has the highest barrier level. The findings of this article may offer decision support to stakeholders for mitigating the barriers to implementing a resilient supply chain in the food industry.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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