一种求解DEA决策单元有效性的算法

为了降低求解DEA模型决策单元有效性的误差及简化求解DEA模型决策单元有效性运算，在传统DEA模型线性规划的基础上，首先，消除了传统DEA模型线性规划中的非阿基米德无穷小，将传统DEA模型的线性规划转化为无非阿基米德无穷小的线性规划.其次，引入Poset集合，将无非阿基米德无穷小的线性规划转化为简约线性规划.最后给出一个数值实例，用新算法对实例进行计算.结果表明，新算法消除了非阿基米德无穷小，能够降低计算误差，而引入Poset集合降低了DEA模型的运算次数.

An Algorithm for the Effectiveness of DEA Decision Making Units

In order to reduce the error in solving the effectiveness of DEA decision making units (DMU''s) and to simplify the computation concerned, this paper applied the linear programming of traditional DEA models as the basis, and eliminated the non-Archimedean infinitesimal in the linear programming of traditional DEA models, which was converted into a linear programming without non-Archimedean infinitesimal, and then, further converted into a simple linear programming by introducing Poset set. At last, a numerical example was given and calculated with this new algorithm. The result reveals that this new algorithm eliminates the non-Archimedean infinitesimal and is able to reduce calculation errors. Meanwhile, the introduction of the Poset set reduces the operations of DEA models.