[2510.20052] Endogenous Aggregation of Multiple Data Envelopment Analysis Scores for Large Data Sets

Mathematics > Optimization and Control arXiv:2510.20052 (math) [Submitted on 22 Oct 2025 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Endogenous Aggregation of Multiple Data Envelopment Analysis Scores for Large Data Sets Authors:Hashem Omrani, Raha Imanirad, Adam Diamant, Utkarsh Verma, Amol Verma, Fahad Razak View a PDF of the paper titled Endogenous Aggregation of Multiple Data Envelopment Analysis Scores for Large Data Sets, by Hashem Omrani and 5 other authors View PDF Abstract:We propose an approach for dynamic efficiency evaluation across multiple organizational dimensions using data envelopment analysis (DEA). The method generates both dimension-specific and aggregate efficiency scores, incorporates desirable and undesirable outputs, and is suitable for large-scale problem settings. Two regularized DEA models are introduced: a slack-based measure (SBM) and a linearized version of a nonlinear goal programming model (GP-SBM). While SBM estimates an aggregate efficiency score and then distributes it across dimensions, GP-SBM first estimates dimension-level efficiencies and then derives an aggregate score. Both models utilize a regularization parameter to enhance discriminatory power while also directly integrating both desirable and undesirable outputs. We demonstrate the computational efficiency and validity of our approach on multiple datasets and apply it to a case study of twelve hospitals in Ontario, Canada, evaluating three theoretically grounded dime...