A fuzzy programming-based evaluation process and a decision support system proposal for project budget allocation

Improving the effectiveness and strategic consistency of research and development evaluation and funding decisions is a critical requirement for the efficient use of public resources. In current practice, obsolete evaluation criteria with subjectively assigned weights and inadequate alignment with institutional priorities pose a substantial challenge that reduces the effectiveness of resource allocation. The significance of criteria determined through expert consultation and literature review was objectively assessed using Shannon entropy, and a systematic evaluation methodology was established. Project proposals were classified into specific profile groups utilizing the k-means clustering algorithm, and a fuzzy mathematical programming model was formulated to optimize budget allocation based on these profiles and evaluation scores. A user-friendly decision support system was designed and integrated into the evaluation workflow to enhance practical applicability in institutional contexts. The findings indicate that the suggested methodology facilitates expedited, clearer, more traceable, and strategically aligned assessments, with sensitivity studies validating the model's resilience. These findings significantly enhance institutional decision-making, improve alignment with national priorities, and strengthen the efficiency and traceability of R&D support mechanisms.