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This study proposes the multi-objective programming (MOP) method for solving network DEA (NDEA) models. In the proposed method, the divisional efficiencies (within an organization) and the overall efficiency of the organization are formulated as separate objective functions in the multi-objective programming model. Compared with conventional DEA where the intermediate processes and products are ignored, this work measures the organization's overall efficiency without neglecting the efficiencies of its subunits. Two case studies demonstrate the proposed NDEA–MOP's utility in measuring the efficiencies of an organization with concerning interactive internal process. [ABSTRACT FROM AUTHOR]

This study develops the hybrid models of dynamic multidimensional efficiency classification. By integrating data envelopment analysis (DEA), naïve Bayesian networks (NBN) and dynamic Bayesian networks (DBN), this work proposes a five-step design for efficiency classification: (1) performance evaluation with DEA model, (2) efficiency discretization, (3) intra-period classification by NBN, (4) inter-period classification by DBN, (5) testing and validation. Due to the Markovian property of the dynamic models, the inter-period dependency is assumed invariant over time. In data-driven parameter learning, the fuzzy parameters for incorporating the variation in dynamic dependencies are introduced. We conduct an empirical case study of higher education in Taiwan to demonstrate the usability of this design. [ABSTRACT FROM AUTHOR]