Computing gradient-based stepwise benchmarking paths
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Elsevier
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In this paper, a new stepwise benchmarking approach is presented. It is based on the concept of effi-ciency field potential given by a continuous and differentiable function that decreases monotonously as the amount of inputs consumed is reduced and the amount of outputs produced is increased. A gradientbased stepwise efficiency improvement method is proposed and the graphical interpretation of the continuous gradient-based trajectories is shown. A minimum potential DEA model is also formulated. The proposed approach is units invariant and can take into account preference structure, non-discretionary variables and undesirable outputs. The proposed method has been applied to an organic farming dataset
In this paper, a new stepwise benchmarking approach is presented. It is based on the concept of effi-ciency field potential given by a continuous and differentiable function that decreases monotonously as the amount of inputs consumed is reduced and the amount of outputs produced is increased. A gradientbased stepwise efficiency improvement method is proposed and the graphical interpretation of the continuous gradient-based trajectories is shown. A minimum potential DEA model is also formulated. The proposed approach is units invariant and can take into account preference structure, non-discretionary variables and undesirable outputs. The proposed method has been applied to an organic farming dataset
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Belarmino Adenso García Fernández es el investigador principal del proyecto "Análisis y diseño de redes logísticas eficientes, robustas y sostenibles"
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This research was carried out with the financial support of the Spanish Ministry of Science and the European Regional DevelopmentFund (ERDF), grant DPI2013-41469-P.
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