Balancing Sustainability and Decision Maker Preferences in Regional Development Location Selection: A Multi-criteria Approach Using AHP and Fuzzy Goal Programming
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we address the challenge of balancing sustainability and decision-maker preferences in regional development location selections.We propose a multi-criteria decision-making framework combining the Analytical Hierarchy Process (AHP) and Fuzzy Goal Programming (FGP) to evaluate potential sites.AHP is utilized to prioritize criteria, incorporating both quantitative and qualitative factors, while FGP allows for the accommodation of uncertainty and conflicting goals.Our findings reveal that this integrated approach provides a robust, systematic method for identifying optimal locations that align with both sustainability goals and stakeholder priorities.The analysis revealed the following satisfaction levels: Price 61.11%, Quality 80.4%, Delivery Time 79.3%, Carbon Emission 91.76%, and Preference 51.18%.The findings emphasize the complex process of selecting vendors within the palm oil supply chain.The implications of this research suggest enhanced decision-making efficiency and effectiveness in regional planning, promoting sustainable development practices.
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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 | 0.004 | 0.001 |
| 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 | 0.001 | 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 it