A hybrid BWM–TOPSIS approach for preferencing evaluation of sustainable and conventional products
Bibliographic record
Abstract
In recent years, governments have sought to find sustainable solutions that would have a positive impact economically, environmentally, and socially. Remanufacturing is a promising solution as remanufactured products help sustainability by saving resources, like using less raw materials, cutting emissions from traditional manufacturing, lowering the amount of landfill waste, and offering a cost-effective alternative product. This paper studies the preferences of people in the Kingdom of Saudi Arabia between new and remanufactured products across three categories: electronics, car parts, and furniture. The products were evaluated based on four factors: quality, price, availability, and warranty. This research used the Best-Worst Method and Technique for Order Preference by Similarity to Ideal Solution together for the analysis. For all the product categories, the findings showed that warranty is the most weighted criteria consumers will rely on to select between the new and remanufactured products. However, consumers prefer new products over the remanufactured ones for all the product categories. Supply chain decision-makers are required to optimize the pricing of these products to increase the popularity of these products.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".