Analyzing the barriers to resilience supply chain adoption in the food industry using hybrid interval-valued fermatean fuzzy PROMETHEE-II model
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".