Stochastic Data Envelopment Analysis Model With Imprecise Data And Its Application

Data Envelopment Analysis (DEA) is an intersectional field of operation research, management science and mathematical economics. DEA depends on mathematical programming to evaluate the relative efficiency of DMUs with multiple inputs and multiple outputs. In this dissertation, the development of DEA theories, DEA methods and DEA application are discussed. The basic DEA models and DEA efficiency theories are analyzed.Lots of achievements have been made in many respects, using traditional DEA methods. However, in fact some DEA problems with imprecise data exist in the world, which can't be solved by traditional methods. After analyzing the method of transforming imprecise data into precise data, IDEA model, based on C~2R-input DEA model, is built. Because C~2R-input DEA model is radial and a lot of uncertain factors should be taken into consideration when making a decision about production management, namely stochastic factors. As for the above reason, a stochastic IDEA model is contributed to deal with the existing problems. And then replace the radial C~2R model with a non-radial stochastic IDEA model which is based on Russell Graph Measure. The new model extends the applying areas.In the last chapter, a numerical example is employed to confirm the applicability of the new stochastic IDEA method. From the computing results, the conclusion can be made, that the new method is useful in realistic appraisal of the productive efficiencies, for its consistence with the production result.