Bioethanol sustainable supply chain design: A multi-attribute bi-objective structure
Bibliographic record
Abstract
To design a bioethanol supply chain, along with the transportation and operational costs, it is vital to consider more factors categorized into three sustainability pillars (i.e. economy, social and environment). In this paper, to develop a mathematical model for bioethanol supply chain (BSC) , we propose a two-phase methodology; in the first phase, using a sustainable framework of attributes contributing to the facility location selection in the BSC network, we calculate the sustainability score of alternatives through employing the best-worst method (BWM). Then, considering the results of the multi-attribute step as the parameters of an objective function called the sustainability value function, we develop a bi-objective multi-level bioethanol supply chain model. To solve the proposed model, a Nested bi-objective Optimization Genetic Algorithm (NbOGA) is introduced in this research. Finally, we evaluate the performance of the presented BSC model and the algorithm for a real-world problem. The results show that using the proposed structure, both sustainability attributes and transportation costs are appropriately satisfied in the BSC network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".