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    Sequential data envelopment analysis
    Rolf Färe, Valentin Zelenyuk
    Abstract: We consider a new class of Data Envelopment Analysis (DEA) modeling, which we call ‘sequential DEA’. This new approach is a relatively simple generalization of the standard and popular in practice DEA. It allows for analyzing efficiency of the decision making units that consist of a sequence of sub-DMUs (e.g., branches of banks, hospital holding company running a number of hospitals at different locations, hotel chains, etc.). The approach is embedded in the Hilbert sequence space (ℓ2) and therefore it allows for potentially different numbers of the sub-DMUs as well as different numbers of inputs and outputs used by different decision making units. We hope this approach will open up a new stream of literature in the sense that many existing variations from the already rich literature on DEA can be adapted to this approach.


    Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources
    Authors: Zhongbao Zhou, Meng Gao, HeluXiao, Rui Wang, Wenbin Liu
    Abstract: The existing literature suggests that the out-of-sample performance of traditional mean-variance portfolio strategies is not robust, and their performance is even inferior to that of the equal weight strategy. To address this problem, this paper first clarifies that a complete investment process consists of two parts, namely, stock selection and investment weight ...


    An integrated bi-objective data envelopment analysis model for measuring returns to scale
    Mushtaq Taleba, Ruzelan Khalid, Razamin Ramli, Mohammad Reza Ghasemi, Joshua Ignatius
    Abstract: Classical efficiency studies on data envelopment analysis (DEA) consider all its inputs and outputs are desirable factors and real valued-data. Additionally, the DEA models only focus either on input-oriented projection minimizing inputs for an inefficient decision making unit (DMU) while keeping outputs at their maximum level, or output-oriented projection maximizing outputs ...


    Measuring efficiency in a general production possibility set allowing for negative data: An extension and a focus on returns to scale
    Kao Chiang
    Abstract: Data envelopment analysis (DEA) is a technique used to measure the relative efficiency of a set of production units that applies multiple inputs to produce multiple outputs. In its original settings, the data is required to be nonnegative. To allow for negative data, several methods have been proposed. While these methods have merits, they also have weaknesses and limitations. This paper generalizes the construction of the production possibility set from production units with nonnegative observations to those with real values. Given the signs of the aggregate target ...


    Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application
    Adel Hatami-Marbini, Aliasghar Arabmaldar
    Abstract: Measuring economic and cost efficiency receives ever-increasing attention of the executives and managers of small-and medium-sized enterprises (SMEs) to minimise total production costs. The conventional Farrell cost efficiency (CE) as a key determinant requires the precise information on inputs, outputs and input prices, while in praxis uncertainty is inherent and inevitable in data and its negligence conceivably results ...


    Production scale-based two-stage network data envelopment analysis
    Junfei Chu, Joe Zhu
    Abstract: We develop a new network data envelopment analysis (DEA) approach for two-stage network systems considering a match between the production scale of the substages and the intermediate measure levels. Several explicit production axioms are introduced to build a production possibility set. New models are developed based on the production possibility set and a frontier projection procedure with the production scale matching process. Unlike the existing approach which assumes ...


    Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach
    Lin Zhang, Linlin Zhaoa, Yong Zha
    Abstract: Industrial air pollution control recently becomes a major policy issue in China. Performance evaluation can examine policy effectiveness and provide decision support for industrial development. A regional industrial system in China contains production and abatement stages. Within this structure, the capacity of industrial waste gas treatment can be treated as a carry-over variable. More precisely, it is a desirable output of the abatement stage in the previous period but an input to the abatement stage in the current period. Using this framework, this study establishes a dynamic two-stage data envelopment analysis model to explore the efficiencies of regional industrial systems in China. This model provides measures ...






    Date: : 2021/05/09
    VisitedL: 1002


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