Optimizing the Selection of the Sustainable Micro, Small, and Medium-Sized Enterprises Development Center Using a Multi-Criteria Approach for Regional Development
Bibliographic record
Abstract
This research presents an integrated Analytic Hierarchy Process (AHP) and Fuzzy Goal Programming (FGP) model for optimizing the selection of Micro, Small, and Medium-Sized Enterprise (MSME) development centers in support of sustainable regional growth.The model incorporates multiple economic, environmental, and social criteria, including Initial Investment Cost, Revenue Potential, Environmental Impact, and Job Creation.Using case studies from Regional A and Regional B, the proposed model evaluates the performance of MSME centers by comparing their scores across various criteria.The results indicate that Regional B (Center 2) consistently outperforms Regional A (Center 1), achieving full membership values across key criteria such as Operating Cost, Revenue Potential, and Innovation and Technology Adoption, reflecting a strong alignment with sustainability goals.In contrast, Regional A demonstrates underperformance in areas like Resource Utilization and Social Inclusion.These findings suggest that Regional B is better suited for MSME development in terms of sustainability and long-term regional growth.The model's flexibility allows for the integration of stakeholder preferences and regional priorities, offering a robust decisionmaking framework for policymakers.
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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 | 0.000 |
| 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".