인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study proposes a machine learning-based approximation framework to alleviate the substantial computational burden of two-stage network Data Envelopment Analysis (DEA) in large-scale applications. Although network DEA provides a more realistic representation of multi-stage production processes than conventional single-stage models, its computational cost grows rapidly as the number of decision-making units (DMUs) increases, limiting its applicability in big-data environments. To address this challenge, the proposed framework evaluates only a strategically selected subset of DMUs using an exact two-stage network DEA model and subsequently trains supervised learning models to approximate stage-wise and overall efficiency scores for the remaining units. Both regression and classification approaches are considered to capture efficiency levels and frontier membership. In addition, a frontier projection-based data augmentation scheme is introduced to mitigate data sparsity near the efficient frontier and improve model generalization. Extensive simulation experiments demonstrate that the proposed approach reduces computational time by up to 90% while preserving high predictive performance. In particular, tree-based models achieve coefficients of determination exceeding 0.95, and efficiency classification yields F1 scores above 0.98. These findings indicate that the proposed framework enables scalable and near-real-time efficiency evaluation in complex network production systems, thereby extending the practical applicability of network DEA to large-scale and data-intensive decision-making contexts.
본문·목차
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UCI(KEPA) : I410-151-26-02-095550766
