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A consensual peer-based DEA-model with optimized cross-efficiencies - Input allocation instead of radial reduction

机译:基于共识的基于对等方的DEA模型,具有优化的交叉效率-输入分配而不是径向减少

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摘要

Data Envelopment Analysis DEA is a method for estimating (in-)efficiencies of Decision Making Units DMUs by means of weighted output - to input - ratios, being the weights optimal virtual prices of such ex-post activities for all units. The cross-efficiency matrix then evaluates these output - to input - relations with respect to all optimal price systems, and hence permits efficiency rankings for the DMUs by aggregating the matrix entries line - and/or columnwise. In this contribution the classical input oriented DEA approach is generalized twofold: its first aim is an optimal efficiency improving input allocation rather than a mere radial input reduction. The second aim is the choice of a peer-DMU, the price system of which is acceptable for the remaining units. As free input allocation permits substitutional effects and so rises productivities in view of possible peers and for all units, it supports such consensual choice. Numerical examples show the positive effects of the new concept.
机译:数据包络分析DEA是一种通过加权产出与投入之比(即所有单位此类事后活动的最优虚拟价格的权重)估算决策单位DMU效率的方法。交叉效率矩阵然后评估与所有最优价格系统有关的这些产出-输入-关系,并因此允许通过汇总矩阵输入行和/或列来对DMU进行效率排名。在这一贡献中,经典的面向输入的DEA方法被概括为两个方面:其第一个目标是提高输入分配的最佳效率,而不仅仅是减少径向输入。第二个目标是选择对等DMU,其价格体系对于其余单位可以接受。由于自由投入分配允许替代效应,因此考虑到可能的同伴,并且对于所有单位提高了生产率,它支持这种自愿选择。数值示例表明了新概念的积极作用。

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