A multi-objective optimization model of a closed-loop supply chain for supplier selection and order allocation under uncertainty: A case study of retail stores for protein products
Bibliographic record
Abstract
The supply chain plays an essential role in the competition between companies. Supplier selection is of great importance considering the influence on the quality of the final product, the return rate, the price of the product, and the sustainability of the whole supply chain. Moreover, the real world is facing much uncertainty. In this uncertain environment, applying fuzzy decision support systems is promising. The main objective of this study is to develop an optimization model to choose suppliers and determine the number of orders for perishable protein products in uncertain conditions in a retail store. A fully fuzzy multi-objective model for a retailer company's closed-loop supply chain is developed to minimize costs and waste and maximize profit, customer satisfaction, quality, and margin under uncertainty. The proposed model is applied in a real case study of Iranian retail stores for protein products. The results proved the potential of the proposed model to improve the closed-loop supply chain's sustainability performance.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".